# kb

id: code-review-20260505-0
file: data/en.wikipedia.org/wiki/Code_review-0.md
category: reference
title: Code review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Code_review
date_saved: 2026-05-05T09:52:53.547978+00:00
instance: kb-cron
parent: code-review-20260505-0
summary:
Code review (sometimes referred to as peer review) is a software quality assurance activity in which one or more people examine the source code of a computer program, either after implementation or during the development process. The persons performing the checking, excluding the author, are called "reviewers". At least one reviewer must not be the code's author. Code review differs from related software quality assurance techniques like static code analysis, self-checks, testing, and pair programming. Static analysis relies primarily on automated tools, self-checks involve only the author, testing requires code execution, and pair programming is performed continuously during development rather than as a separate step. == Goal == Although direct discovery of quality problems is often the main goal, code reviews are usually performed to reach a combination of goals: Improving code quality – Improve internal code quality and maintainability through better readability, uniformity, and understandability Detecting defects – Improve quality regarding external aspects, especially correctness, but also find issues such as performance problems, security vulnerabilities, and injected malware Learning/Knowledge transfer – Sharing codebase knowledge, solution approaches, and quality expectations, both to the reviewers and the author Increase sense of mutual responsibility – Increase a sense of collective code ownership


id: commentary-article-20260505-0
file: data/en.wikipedia.org/wiki/Commentary_article-0.md
category: reference
title: Commentary article
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Commentary_article
date_saved: 2026-05-05T09:52:54.688511+00:00
instance: kb-cron
parent: commentary-article-20260505-0
summary:
A commentary article is a short narrowly focused article that is usually commissioned by an Academic journal, though they are sometimes published from unsolicited submissions. == References ==


id: funding-by-lottery-20260505-0
file: data/en.wikipedia.org/wiki/Funding_by_lottery-0.md
category: reference
title: Funding by lottery
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Funding_by_lottery
date_saved: 2026-05-05T09:52:55.911338+00:00
instance: kb-cron
parent: funding-by-lottery-20260505-0
summary:
Funding by lottery is the use of randomization in the allocation of science funding by research funding organizations. As of early 2025, about a dozen funders worldwide have implemented some form of funding by lottery in their funding calls. There is no scholarly consensus on the benefits and drawbacks of funding by lottery. Proponents argue that it can help mitigate biases in funding allocation and minimize the high costs associated with grant writing and peer review. Critics, however, express concerns that diminishing the role of peer review panels in funding decisions could lead to a decline in the quality of funded projects and undermine public trust in funders, in scientific peer review, and ultimately in science at large. == Overview == Science funding is generally distributed through competitive funding calls organized by research funding agencies (commonly referred to as "funders"). Eligible researchers submit research grant proposals in response to these calls, which are then evaluated by expert peer review panels. Funding is typically awarded to the proposals that receive the most positive evaluations from these panels. Compared to this traditional, peer-reviewed science funding, funding by lottery fully or partly replaces peer review panel evaluations with random selection. In a full


id: funding-by-lottery-20260505-1
file: data/en.wikipedia.org/wiki/Funding_by_lottery-1.md
category: reference
title: Funding by lottery
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Funding_by_lottery
date_saved: 2026-05-05T09:52:55.911338+00:00
instance: kb-cron
parent: funding-by-lottery-20260505-0
summary:
=== Arguments in favor === The main argument in favor of funding by lottery is that randomization can help reduce the costs of grant writing and peer review while promoting fairer and more diverse funding outcomes. More specifically, funding by lottery may curb or eliminate biases in research evaluation, such as reviewer conservatism – i.e. the tendency to favor conventional ideas over more innovative or riskier ones – as well as bias against interdisciplinary research. Furthermore, funding by lottery is seen as way to counteract the growing concentration of research funding in the hands of few renowned labs and research-performing institutions, a phenomenon known as the "Matthew effect" in science funding. Lastly, some have argued that funding by lottery could remove some incentives for research misconduct. Because grant acquisition is considered a marker of academic success, researchers may feel pressured to obtain as much funding as possible. In extreme cases, this can lead to unethical practices, like submitting virtually identical research grant proposals to multiple funding calls – a problem known as "double-dipping". By decoupling grant acquisition from academic prestige, funding by lottery could help mitigate such incentives for misconduct. === Arguments against === Some scholars highlight the lack of


id: ingelfinger-rule-20260505-0
file: data/en.wikipedia.org/wiki/Ingelfinger_rule-0.md
category: reference
title: Ingelfinger rule
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ingelfinger_rule
date_saved: 2026-05-05T09:52:57.068716+00:00
instance: kb-cron
parent: ingelfinger-rule-20260505-0
summary:
The Ingelfinger rule is an eponymous rule named after Franz J. Ingelfinger, The New England Journal of Medicine (NEJM) editor-in-chief who enunciated it in 1969. The rule, as originally articulated in the editorial "Definition of 'Sole Contribution'", stated that NEJM would not publish findings that had been published elsewhere. Though originally meant only for NEJM, the guideline was subsequently adopted by several other scientific journals, and it has shaped scientific publishing ever since. Historically it has also helped to ensure that the journal's content is fresh and does not duplicate content previously reported elsewhere, and it seeks to protect the scientific embargo system. A similar policy had been earlier expressed in 1960 by Samuel Goudsmit, editor of the Physical Review Letters, but it did not become as well known. The Ingelfinger rule has been seen as having the aim of preventing authors from performing duplicate publications which would unduly inflate their publication record. On the other hand, it has also been stated that the real reason for the Ingelfinger rule is to protect the journals' revenue stream, and with the increase in popularity of preprint servers such as arXiv, bioRxiv, and HAL many journals have loosened their requirements concerning the


id: interdisciplinary-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Interdisciplinary_peer_review-0.md
category: reference
title: Interdisciplinary peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interdisciplinary_peer_review
date_saved: 2026-05-05T09:52:58.269045+00:00
instance: kb-cron
parent: interdisciplinary-peer-review-20260505-0
summary:
Interdisciplinary Peer Review (IPR) is a peer review process with an additional focus outside of the area of the author's subject of expertise. Disciplines such as telecommunications, political science, engineering, and medicine require specific subject matter expertise, however, they still cross multiple disciplines and may require review from many alternate functional areas to achieve maximum perspective to prevent duplication or improper publication. Reviews of this nature may also cross cultures, race, and other demographics to gain perspective. == Reduplication and Interdisciplinarity == In Peer Commentary on Peer Review: A case study in scientific quality control, Stevan R. Harnard (p. 15) begins to touch on the concept by addressing how well the review process works and what factors prevent re-duplication. Julie Klein, a professor of interdisciplinary studies at Wayne State University, defined interdisciplinarity as "new divisions of intellectual labor, collaborative research, team teaching, hybrid fields, comparative studies, increased borrowing across disciplines, and a variety of unified, holistic perspectives that have created pressures upon traditional divisions of knowledge". Klein has also described how interdisciplinarity is used to "find answers to complex questions, address broad issues, explore disciplinary and professional relations, to solve problems beyond the scope of any one discipline, and to


id: journalreview-org-20260505-0
file: data/en.wikipedia.org/wiki/JournalReview.org-0.md
category: reference
title: JournalReview.org
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/JournalReview.org
date_saved: 2026-05-05T09:52:59.408510+00:00
instance: kb-cron
parent: journalreview-org-20260505-0
summary:
JournalReview.org was an online interdisciplinary journal club. It hosted an international community of doctors and existed for the purpose of facilitating critical discussion and post-publication peer review of all medical literature indexed by the National Library of Medicine in PubMed. It was part of the movement towards open peer review, telemedicine, and self-archiving/open access. == Contents == It was web-based and consisted of commentary and discussion related to published medical literature. Members could participate individually, or by creating their own "journal clubs" – collections of members who were actively participating in discussion of mutual interest. Some residency training programs adopted this resource to meet ACGME requirements. Members utilized this resource to suggest future research, and to identify bias or limitations to published work. Educators utilized this resource to help stimulate and document critical discussion of assigned journal articles. Authors utilized this resource to answer questions about and provide supplement to their published work. This resource was also utilized by the public as a vehicle to better understand published trade literature. Members consists of all the major specialties as well areas of basic science. Approximately 7,000 doctors, health care professionals, researchers, residency training programs, and students from all over the world


id: lehman-review-20260505-0
file: data/en.wikipedia.org/wiki/Lehman_Review-0.md
category: reference
title: Lehman Review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Lehman_Review
date_saved: 2026-05-05T09:53:00.564964+00:00
instance: kb-cron
parent: lehman-review-20260505-0
summary:
A Lehman Review is an independent peer review and evaluation of the status of a major construction project in the United States Department of Energy's (DOE's) Office of Science. Lehman Reviews evaluate all aspects of a construction project's current status, including technical aspects, cost, schedule and management, and they are usually held twice a year. Lehman Reviews are widely known in DOE, other agencies and abroad. The reviews are named after Daniel Lehman, Director of the DOE Office of Science's Office of Project Assessment since 1991. == History == The Office of Project Assessment within the DOE's Office of Science provides independent advice relating to those activities essential to the construction and operation of major research facilities, as well as professional management and staff support regarding these functions. Within this mission, the office conducts independent technical, cost, schedule and management peer reviews of major construction projects, which include civil construction projects and the construction of large experimental equipment. While the Office of Science is exempt from DOE Order 413.3B, Program and Project Management for the Acquisition of Capital Assets for independent project and peer review, a Lehman Review will typically meet the requirements of the order. During a project's proposal


id: henry-oldenburg-20260505-0
file: data/en.wikipedia.org/wiki/Henry_Oldenburg-0.md
category: reference
title: Henry Oldenburg
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Henry_Oldenburg
date_saved: 2026-05-05T09:53:01.743243+00:00
instance: kb-cron
parent: henry-oldenburg-20260505-0
summary:
Henry Oldenburg (also Henry Oldenbourg) (c. 1618 as Heinrich Oldenburg – 5 September 1677) was a German theologian, diplomat, and natural philosopher, known as one of the creators of modern scientific peer review. He was one of the foremost intelligencers of 17th-century Europe, with a network of correspondents to rival those of Fabri de Peiresc, Marin Mersenne, and Ismaël Boulliau. At the foundation of the Royal Society in London, he took on the task of foreign correspondence, as the first Secretary. == Early life == Born in Bremen, Germany, he was trained in theology and received his degree from the local Gymnasyum illustre on 2 November 1639. He had an initial very firm grasp of the German, Latin, and Greek languages. His movements during the 1640s are unclear, but he is thought to have worked as a tutor in England for much of the decade. In 1648 he left England and spent some time in Leiden and Utrecht in the Dutch Republic, where he became conversant in the Dutch language. After a short stay back in Bremen in the spring, he arrived back in London in July 1653 as a diplomatic envoy of the city of Bremen's senate to the


id: open-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Open_peer_review-0.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T09:53:02.960954+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
Open peer review is the various possible modifications of the traditional scholarly peer review process. The three most common modifications to which the term is applied are: Open identities: Authors and reviewers are aware of each other's identity. Open reports: Review reports are published alongside the relevant article (rather than being kept confidential). Open participation: The wider community (and not just invited reviewers) are able to contribute to the review process. These modifications are supposed to address various perceived shortcomings of the traditional scholarly peer review process, in particular its lack of transparency, lack of incentives, wastefulness, bullying and harassment. == Definitions == Open identities Open peer review may be defined as "any scholarly review mechanism providing disclosure of author and referee identities to one another at any point during the peer review or publication process". Then reviewer's identities may or may not be disclosed to the public. This is in contrast to the traditional peer review process where reviewers remain anonymous to anyone but the journal's editors. Authors' names are disclosed during the process in a single-blind organisation of reviews. In the double-blind process, authors' names and reviewers' names all remain anonymous except to the editor. Open reports Open


id: open-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Open_peer_review-1.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T09:53:02.960954+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
== History == In 1997, the open participation journal Electronic Transactions on Artificial Intelligence was launched. Articles were first "published" (meaning the day when the article is first made public), then "reviewed" (meaning a open process of public discussion), and then "refereed" (meaning the confidential pass/fail decision of whether to accept the article or not). In 1999, the open access journal Journal of Medical Internet Research was launched, which from its inception decided to publish the names of the reviewers at the bottom of each published article. Also in 1999, the British Medical Journal moved to an open peer review system, revealing reviewers' identities to the authors but not the readers, and in 2000, the medical journals in the open access BMC series published by BioMed Central, launched using open peer review. As with the BMJ, the reviewers' names are included on the peer review reports. In addition, if the article is published the reports are made available online as part of the "pre-publication history"'. Several other journals published by the BMJ Group allow optional open peer review, as does PLoS Medicine, published by the Public Library of Science. The BMJ's Rapid Responses allows ongoing debate and criticism following publication.


id: open-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Open_peer_review-2.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T09:53:02.960954+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
Publishers: BioMed Central BMJ Group Copernicus Publications European Molecular Biology Organization (EMBO) Frontiers MDPI (authors have the option to publish peer review reports, etc.) Nature Research Open Research Europe (the European Union publishing platform for Horizon Europe and Horizon 2020 projects) Journals: eLife GigaScience Nature PeerJ PLOS ReScience C Semantic Web journal by IOS Press WikiJournal SciPost Peer review at The BMJ, BioMed Central, EMBO, eLife, ReScience C, and the Semantic Web journal involves posting the entire pre-publication history of the article online, including not only signed reviews of the article, but also its previous versions and in some cases names of handling editors and author responses to the reviewers. Furthermore, the Semantic Web journal publishes reviews of all submissions, including rejected ones, on its website, while eLife plans to publish the reviews not only for published articles, but also for rejected articles. The European Geosciences Union operates public discussions where open peer review is conducted before suitable articles are accepted for publication in the journal. Sci, an open access journal which covers all research fields, adapted a post publication public peer-review (P4R) in which it promised authors immediate visibility of their manuscripts on the journal's online platform after a


id: peer-critique-20260505-0
file: data/en.wikipedia.org/wiki/Peer_critique-0.md
category: reference
title: Peer critique
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_critique
date_saved: 2026-05-05T09:53:04.124599+00:00
instance: kb-cron
parent: peer-critique-20260505-0
summary:
Peer critique, a specialized form of critique, is the common practice of professional peers, especially writers, reviewing and providing constructive criticism of each other's work before that work is turned in for credit or professional review. Writers in many genres and professions, including fiction writers and technical writers, use some form of peer critique as part of their process of writing. It is also commonly used as an instructional technique in school writing settings. Peer critique may also be referred to as peer review or a writing workshop. Writers who gather to critique each others' work are often called writing circles. == In action == === In the classroom === Peer critique has long been used as part of the process of teaching writing from primary school to secondary and post-secondary education. In traditional classrooms power and authority can often be teacher-centric, with teachers correcting work to their own vision of ideal writing. Many researchers have found that peer critique offers a complementary style of feedback Whereas teacher feedback may focus on general comments and error correction, peers tend to give specific, deep comments on the work before them rather than correcting to an ideal. Peer critique, furthermore, is used


id: peer-critique-20260505-1
file: data/en.wikipedia.org/wiki/Peer_critique-1.md
category: reference
title: Peer critique
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_critique
date_saved: 2026-05-05T09:53:04.124599+00:00
instance: kb-cron
parent: peer-critique-20260505-0
summary:
=== Online writing classes === In recent years with the advent of the Blackboard Learning System, Canvas, and similar online teaching tools (see LMS), it has become possible to take writing courses entirely online. In online courses, students generally give each other feedback on writing in message-board style posts, though some instructors may allow for anonymity in posting responses. Comments are usually brief. Teachers must beware of the "pile-on effect" of students merely echoing what teachers and previous commenters have mentioned; it will be useful for teachers to apply lessons from peer critique websites, which have functioned online for many years. Some instructors may, for example, arrange students into small synchronous groups for discussion, mitigating overwhelming feedback from the entire class. === Online critique sites === Since at least 1985, with the Compuserve Books & Writer's Forum, writers have formed writing spaces online where they can discuss writing, share resources, and critique work. There are many active critique sites now, catering to all levels and genres of writers; the popular website Reddit has a sub-reddit dedicated to writing critique, titled critique my writing., while another popular forum, www.writingforums.com, is dedicated to writing and online critique. Other peer critique sites include


id: peer-jury-process-20260505-0
file: data/en.wikipedia.org/wiki/Peer_jury_process-0.md
category: reference
title: Peer jury process
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_jury_process
date_saved: 2026-05-05T09:53:05.292981+00:00
instance: kb-cron
parent: peer-jury-process-20260505-0
summary:
The peer jury process is often used in the arts to assess applications for grants, proposals for exhibitions made in response to an open call for submissions and art competitions. Like the practice of peer review used in the academic sphere, a peer jury brings to an evaluation process expertise that an organization or agency awarding grants or sponsoring a competition might not itself have. A peer jury in the arts context is generally composed of artists selected to be representative of those working in a relevant discipline or area. While jurors are expected to have independence, anonymity is less of a concern than in academic peer review, in part because the jury's task is not to catch errors or offer criticism but to only to select. For example, in Canada, agencies that award grants typically publish lists of jurors or make lists available on request. In principle the peer jury process is an effective way for arts councils and other agencies to be responsive to and engaged with the constituencies they serve, and in touch with the latest developments in contemporary art. == See also == peer review


id: peerage-of-science-20260505-0
file: data/en.wikipedia.org/wiki/Peerage_of_Science-0.md
category: reference
title: Peerage of Science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peerage_of_Science
date_saved: 2026-05-05T09:53:07.483675+00:00
instance: kb-cron
parent: peerage-of-science-20260505-0
summary:
Peerage of Science was a scientific peer review service aimed at improving "the current peer review system and make the peer review process more scientific, fair and transparent". The company was founded in 2011 by the scientists Janne Kotiaho, Mikko Mönkkönen, and Janne-Tuomas Seppänen in Jyväskylä, Finland. Initially it focused on the areas of "ecology, evolutionary biology and conservation biology", but within 2 years it expanded to other areas of science. The service was the winner of the 2012 Award for Publishing Innovation from The Association of Learned and Professional Society Publishers (ALPSP), and of the 2013 recognition award from Communications Professionals of Finnish Universities. Despite patronage from several paying customers, including the Society for Conservation Biology, Springer Nature, and Taylor & Francis, the company failed to expand its market base (it received only 102 submissions in 2017 and only 60% of submissions received at least one publishing offer) and went out of business soon after. == Differences to traditional peer review == The service had several practices that differed from the traditional approaches to academic peer review and submissions. === Concurrent and shared journal consideration === PoS implemented a "unified reviewing process", which reduced "the workload of the reviewers’


id: physicsoverflow-20260505-0
file: data/en.wikipedia.org/wiki/PhysicsOverflow-0.md
category: reference
title: PhysicsOverflow
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PhysicsOverflow
date_saved: 2026-05-05T09:53:08.636905+00:00
instance: kb-cron
parent: physicsoverflow-20260505-0
summary:
PhysicsOverflow is a physics website that serves as a post-publication open peer review platform for research papers in physics, as well as a collaborative blog and online community of physicists. It allows users to ask, answer and comment on graduate-level physics questions, post and review manuscripts from ArXiv (which lists PhysicsOverflow discussion pages among its trackbacks) and other sources, and vote on both forms of content. In addition to the two primary forms of content, the PhysicsOverflow community also welcomes discussions on unsolved problems, and hosts a chat section for discussions on topics generally of interest to physicists and students of physics, such as those related to recent events in physics, physics academia, and the publishing process. == History == PhysicsOverflow was started in April 2014 as a physics-equivalent of MathOverflow by Rahel Knöpfel, a physics PhD at the University of Rostock, high-school student Abhimanyu Pallavi Sudhir, and Roger Cattin, a retired professor of computer science at the University of Applied Sciences, Switzerland. The site was initially a mere question-and-answer forum, as it was started by users dissatisfied by the policies of the Physics Stack Exchange, but it was eventually expanded to include a Reviews section in October 2014. ==


id: preregistration-science-20260505-0
file: data/en.wikipedia.org/wiki/Preregistration_(science)-0.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T09:53:09.860059+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
Preregistration is the practice of registering the hypotheses, methods, or analyses of a scientific study before it is conducted. Clinical trial registration is similar, although it may not require the registration of a study's analysis protocol. Finally, registered reports include the peer review and in principle acceptance of a study protocol prior to data collection. Preregistration has the goal to transparently evaluate the severity of hypothesis tests, and can have a number of secondary goals (which can also be achieved without preregistering ), including (a) facilitating and documenting research plans, (b) identifying and reducing questionable research practices and researcher biases, (c) distinguishing between confirmatory and exploratory analyses, and, in the case of Registered Reports, (d) facilitating results-blind peer review, and (e) reducing publication bias. Although the idea of preregistration is old, the practice of preregistering studies has gained prominence to mitigate certain issues that contribute to the replication crisis in scientific studies. Among others, these issues include publication bias and questionable research practices, such as p-hacking and HARKing. == Types == === Standard preregistration === In the standard preregistration format, researchers prepare a research protocol document prior to conducting their research. Ideally, this document indicates the research hypotheses, sampling procedure,


id: preregistration-science-20260505-1
file: data/en.wikipedia.org/wiki/Preregistration_(science)-1.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T09:53:09.860059+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
=== Creation and development === For many years, scientists and others have worried about reporting biases such that negative or null results from initiated clinical trials may be less likely to be published than positive results, thus skewing the literature and our understanding of how well interventions work. This worry has been international and written about for over 50 years. One of the proposals to address this potential bias was a comprehensive register of initiated clinical trials that would inform the public which trials had been started. Ethical issues were those that seemed to interest the public most, as trialists (including those with potential commercial gain) benefited from those who enrolled in trials, but were not required to "give back," telling the public what they had learned. Those who were particularly concerned by the double standard were systematic reviewers, those who summarize what is known from clinical trials. If the literature is skewed, then the results of a systematic review are also likely to be skewed, possibly favoring the test intervention when in fact the accumulated data do not show this, if all data were made public. ClinicalTrials.gov was originally developed largely as a result of breast cancer consumer lobbying,


id: preregistration-science-20260505-2
file: data/en.wikipedia.org/wiki/Preregistration_(science)-2.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T09:53:09.860059+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
== Benefits == Several articles have outlined the rationale for preregistration (e.g., Lakens, 2019; Nosek et al., 2018; Wagenmakers et al., 2012). The primary goal of preregistration is to improve the transparency of reported hypothesis tests, which allows readers to evaluate the extent to which decisions during the data analysis were pre-planned (maintaining statistical error control) or data-driven (increasing the Type 1 or Type 2 error rate). Meta-scientific research has revealed additional benefits. Researchers indicate preregistering a study leads to a more carefully thought through research hypothesis, experimental design, and statistical analysis. In addition, preregistration has been shown to encourage better learning of Open Science concepts and students felt that they understood their dissertation and it improved the clarity of the manuscript writing, promoted rigour and were more likely to avoid questionable research practices. In addition, it becomes a tool that supervisors can use to shape students to combat any questionable research practices. A 2024 study in the Journal of Political Economy: Microeconomics preregistration in economics journals found that preregistration reduced p-hacking and publication bias if the preregistration was accompanied by a preanalysis plan, but not if the preregistration did not specify the planned analyses. == Criticisms == === Analytical


id: preregistration-science-20260505-3
file: data/en.wikipedia.org/wiki/Preregistration_(science)-3.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T09:53:09.860059+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
=== Practical Implementation === There are also concerns about the practical implementation of preregistration. Many preregistered protocols leave plenty of room for p-hacking (Bakker et al., 2020; Heirene et al., 2021; Ikeda et al., 2019; Singh et al., 2021; Van den Akker et al., 2023), and researchers rarely follow the exact research methods and analyses that they preregister (Abrams et al., 2020; Claesen et al., 2019; Heirene et al., 2021; Clayson et al., 2025; see also Boghdadly et al., 2018; Singh et al., 2021; Sun et al., 2019). In terms of credibility, pre-registered studies are only of higher quality than non-pre-registered studies if the former has a power analysis and higher sample size than the latter but other than that they do not seem to prevent p-hacking and HARKing, as both the proportion of positive results and effect sizes are similar between preregistered and non-preregistered studies (Van den Akker et al., 2023). In terms of adherence, a study of 92 EEG/ERP studies showed that only 60% of studies adhered to their preregistrations or disclosed all deviations. Notably, registered reports had the higher adherence rates (92%) than unreviewed preregistrations (60%). In general, around three-quarters of preregistered studies included at least one


id: publons-20260505-0
file: data/en.wikipedia.org/wiki/Publons-0.md
category: reference
title: Publons
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Publons
date_saved: 2026-05-05T09:53:10.986861+00:00
instance: kb-cron
parent: publons-20260505-0
summary:
Publons was a commercial website that provided a free service for academics to track, verify, and showcase their peer review and editorial contributions for academic journals. It was launched in 2012, bought by Clarivate in 2017 and merged into Web of Science in 2022. It claimed that over 3,000,000 researchers joined the site, adding more than one million reviews across 25,000 journals. In 2019, ResearcherID was integrated with Publons. Publons produced a verified record of a person's review and editorial activity for journals, which could be downloaded to include in CVs, funding and job applications, and promotion and performance evaluations. Publons' business model was based on partnering with publishers. == Background == Publons was founded by Andrew Preston and Daniel Johnston to address the static state of peer-reviewing practices in academic research publishing, in view of encouraging collaboration and speeding scientific development. The Publons name was an homage to the "publon", the "minimum unit of publishable material". The company was registered in New Zealand and had an office in London, UK. It was acquired by Clarivate Analytics in 2017. == Services == Publons also provided: tools for publishers to find, screen, contact, and motivate peer reviewers; data and publications about


id: pubpeer-20260505-0
file: data/en.wikipedia.org/wiki/PubPeer-0.md
category: reference
title: PubPeer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PubPeer
date_saved: 2026-05-05T09:53:12.208470+00:00
instance: kb-cron
parent: pubpeer-20260505-0
summary:
PubPeer is a website that allows users to discuss and review scientific research after publication, i.e. post-publication peer review, established in 2012. The site has served as a whistleblowing platform, in that it highlighted shortcomings in several high-profile papers, in some cases leading to retractions and to accusations of scientific fraud, as noted by Retraction Watch. Contrary to most platforms, it allows anonymous post-publication commenting, a controversial feature which is the main factor for its success. Consequently, accusations of libel have been levelled at some of PubPeer's users; correspondingly the website has since 2016 told commentators to use only facts that can be publicly verified. Questions have been raised about the copyright ownership of PubPeer's often-anonymous contents. In 2021 a study found that "more than two-thirds of comments [on PubPeer] are posted to report some type of misconduct, mainly about image manipulation". Health sciences and life sciences were shown to have most comments, and most comments reporting publishing fraud and data manipulation. Social science and humanities disciplines in turn had fewer comments, but the highest percentage comments about critical reviews about theory and highlight methodological flaws. The research concluded that "while biochemists access the site to report misconduct... social scientists


id: quantitative-biology-journal-20260505-0
file: data/en.wikipedia.org/wiki/Quantitative_Biology_(journal)-0.md
category: reference
title: Quantitative Biology (journal)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quantitative_Biology_(journal)
date_saved: 2026-05-05T09:53:13.415769+00:00
instance: kb-cron
parent: quantitative-biology-journal-20260505-0
summary:
Quantitative Biology is a quarterly peer-reviewed open-access interdisciplinary scientific journal covering experimental biology, theoretical biology, computational biology, bioinformatics, systems biology, synthetic biology, and digital and AI biology. The journal was established in 2013 by Higher Education Press and Tsinghua University. The co-founding editors-in-chief were Michael Q. Zhang and Chao Tang. As of 2019, the editors-in-chief were Chao Tang and Xuegong Zhang. Before 2020, Quantitative Biology was co-published by Higher Education Press and Springer Nature. As of 2023, it was published by Higher Education Press and Wiley. According to the Journal Citation Reports and Scopus, the journal had a 2024 impact factor of 1.4, and a 2024 CiteScore of 2.0, respectively. == Abstracting and indexing == The journal is abstracted and indexed in: Emerging Sources Citation Index (ESCI) Scopus Biological Abstracts BIOSIS Chemical Abstracts Service Embase == References ==


id: registered-report-20260505-0
file: data/en.wikipedia.org/wiki/Registered_report-0.md
category: reference
title: Registered report
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Registered_report
date_saved: 2026-05-05T09:53:14.608295+00:00
instance: kb-cron
parent: registered-report-20260505-0
summary:
A registered report is a scholarly publishing format in which the research question, study design, and analysis plan are peer-reviewed before data are collected or analyzed. This publication model aims to reduce publication bias and improve research transparency by restricting the evaluation of scientific manuscripts to the research question, method, and analysis plan, without consideration of whether the results support the research question or not. == Process == The Registered Reports process occurs in two stages of peer review. In the first stage, known as Stage 1 review, authors submit a detailed study proposal to a journal that offers the Registered Reports format. The proposal typically includes the theoretical rationale, research questions or hypotheses, experimental design, sampling plan, and a preregistered statistical analysis plan. At this point, no data have yet been collected or analyzed. Peer reviewers evaluate the submission based on the importance of the research question and the quality and rigor of the proposed methodology. Because the preregistration undergoes peer review, Registered Reports introduce an additional quality control mechanism compared with standard preregistration practices in which researchers register study plans independently. Following Stage 1 review, a journal may reject the submission, request revisions, or grant in-principle acceptance (IPA).


id: research-integrity-and-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Research_Integrity_and_Peer_Review-0.md
category: reference
title: Research Integrity and Peer Review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_Integrity_and_Peer_Review
date_saved: 2026-05-05T09:53:15.749896+00:00
instance: kb-cron
parent: research-integrity-and-peer-review-20260505-0
summary:
Research Integrity and Peer Review is an international, open access, peer reviewed journal that was launched in 2016. It is published by BioMed Central and focuses on problems in peer review, replication, and the scientific process. == See also == Metascience == References ==


id: al-ruhawi-20260505-0
file: data/en.wikipedia.org/wiki/Al-Ruhawi-0.md
category: reference
title: Al-Ruhawi
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Al-Ruhawi
date_saved: 2026-05-05T09:53:16.940835+00:00
instance: kb-cron
parent: al-ruhawi-20260505-0
summary:
Ishāq bin Ali al-Rohawi (Arabic: إسحاق بن علي الرهاوي) was a 9th-century author of the first medical ethics book in Arabic medicine. His Ethics of the Physician contains the first documented description for peer review processes. The processes gave the fundamentals of peer review processes where practising Arab physicians were reviewed by peers on the medical treatment they provide to their patients. If the treatment of a patient was incorrectly done with negative peer reviews, then the physician is at a lawsuit liability. Al-Ruhawi was probably an Iranian-Nestorian from Al-Ruha, modern-day Şanlıurfa. It is often known as Urfa. Not much is known about Al-Ruhawi, including his beliefs. The author Levey stated in his book that Al-Ruhawi was a Nestorian Christian while Johann Christoph Bürgel wrote that Al-Ruhawi was Jewish. However, both authors did not give evidence to support their argument, rather having it based on their interpretations. Although there are conflictions in these two beliefs, there is evidence to prove that Al-Rahawi had Islamic beliefs. Al-Rahawi began his book with the words "In the name of Allah," which is a traditional opening for Muslims. Additionally, Al-Rahawi uses the word "Allah" hundreds of times in his work, which is associated with


id: scholarly-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-0.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review or academic peer review (also known as refereeing) is the process of having a draft version of a researcher's methods and findings reviewed (usually anonymously) by experts (or "peers") in the same field. Peer review is widely used for helping the academic publisher (i.e., the editor-in-chief, the editorial board, or the program committee) decide whether the work should be accepted, considered acceptable with revisions, or rejected for official publication in an academic journal, a monograph, or in the proceedings of an academic conference. If the identities of authors are not revealed to each other, the procedure is called dual-anonymous peer review. Academic peer review requires a community of experts in a given (and often narrowly defined) academic field, who are qualified and able to perform reasonably impartial review. Impartial review, especially of work in less narrowly defined or inter-disciplinary fields, may be difficult to accomplish, and the significance (good or bad) of an idea may never be widely appreciated among its contemporaries. Peer review is generally considered necessary to academic quality and is used in most major scholarly journals. However, peer review does not prevent publication of invalid research, and as experimentally controlled studies of this process


id: scholarly-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-1.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Workload. A small group of editors/assessors cannot devote sufficient time to each of the many articles submitted to many journals. Miscellany of ideas. Were the editor/assessor to judge all submitted material themselves, approved material would solely reflect their opinion. Limited expertise. An editor/assessor cannot be expected to be sufficiently expert in all areas covered by a single journal or funding agency to adequately judge all submitted material. Reviewers are often anonymous and independent. However, some reviewers may choose to waive their anonymity, and in other limited circumstances, such as the examination of a formal complaint against the referee, or a court order, the reviewer's identity may have to be disclosed. Anonymity may be unilateral or reciprocal (single- or double-blinded reviewing). Since reviewers are normally selected from experts in the fields discussed in the article, the process of peer review helps to keep some invalid or unsubstantiated claims out of the body of published research and knowledge. Scholars will read published articles outside their limited area of detailed expertise, and then rely, to some degree, on the peer-review process to have provided reliable and credible research that they can build upon for subsequent or related research. Significant scandal ensues when an


id: scholarly-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-2.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
to unconditionally accept the manuscript or the proposal, to accept it in the event that its authors improve it in certain ways to reject it, but encourage revision and invite re-submission to reject it outright. During this process, the role of the referees is advisory. The editor(s) is typically under no obligation to accept the opinions of the referees. However, the editor will most often follow the advice of the referees. Furthermore, the referees in scientific publication do not act as a group, do not communicate with each other, and typically are not aware of each other's identities or evaluations. Proponents argue that if the reviewers of a paper are unknown to each other, the editor(s) can more easily verify the objectivity of the reviews. There is usually no requirement that the referees achieve consensus, with the decision instead often made by the editor(s) based on her best judgement of the arguments. In situations where multiple referees disagree substantially about the quality of a work, there are a number of strategies for reaching a decision. The paper may be rejected outright, or the editor may choose which reviewer's point the authors should address. When a publisher receives very positive and


id: scholarly-peer-review-20260505-3
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-3.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
== Recruiting referees == At a journal or book publisher, the task of picking reviewers typically falls to an editor. When a manuscript arrives, an editor solicits reviews from scholars or other experts who may or may not have already expressed a willingness to referee for that journal or book division. Granting agencies typically recruit a panel or committee of reviewers in advance of the arrival of applications. Referees are supposed to inform the editor of any conflict of interests that might arise. Journals or individual editors may invite a manuscript's authors to name people whom they consider qualified to referee their work. For some journals this is a requirement of submission. Authors are sometimes also given the opportunity to name natural candidates who should be disqualified, in which case they may be asked to provide justification (typically expressed in terms of conflict of interest). Editors solicit author input in selecting referees because academic writing typically is very specialized. Editors often oversee many specialties, and can not be experts in all of them. But after an editor selects referees from the pool of candidates, the editor typically is obliged not to disclose the referees' identities to the authors, and in


id: scholarly-peer-review-20260505-4
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-4.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Internal and external peer review === External peer review is the ordinary process of peer review for scholarly journals. The first evidence of formal external peer reviews dates back to 1752, and it became widely used and more standardized after World War II. In this process, scholars who are not beholden to the journal or the author review submitted articles, and in doing so, restrain academic and scientific journals from using solely their own commercial interests as a guide on what to publish. Nature began requiring external peer review in 1973, and other journals quickly followed suit. Internal peer review is having a colleague in the same department or academic institution read over a paper before submitting it to a journal for external peer review. For example, economists in the same government agency may review their peers' papers, as a type of peer professional accountability. People such as Nobel laureate Dan Shechtman and organizations such as the US National Institute of Standards and Technology use internal peer review because they believe that it gives them an opportunity to improve papers before submitting them to a journal. Internal peer review is also recommended for authors with weak writing skills. Unlike


id: scholarly-peer-review-20260505-5
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-5.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Transparent peer review === In transparent peer review the documents relating to the publication of the paper are published alongside the paper. This includes the initial report made by the peer reviewer, the authors' response to it, and the acceptance letter by the editor. The names of the reviewers may still be kept anonymous even in transparent peer review. === Pre- and post-publication peer review === The process of peer review is not restricted to the publication process managed by academic journals. In particular, some forms of peer review can occur before an article is submitted to a journal and/or after it is published by the journal. ==== Pre-publication peer review ==== Manuscripts are typically reviewed by colleagues before submission, and if the manuscript is uploaded to preprint servers, such as ArXiv, BioRxiv or SSRN, researchers can read and comment on the manuscript. The practice to upload to preprint servers, and the activity of discussion heavily depend on the field, and it allows an open pre-publication peer review. The advantage of this method is speed and transparency of the review process. Anyone can give feedback, typically in form of comments, and typically not anonymously. These comments are also public,


id: scholarly-peer-review-20260505-6
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-6.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Result-blind peer review or results blind peer review, first proposed 1966: Reviewers receive an edited version of the submitted paper which omits the results and conclusion section. In a two-stage version, a second round of reviews or editorial judgment is based on the full paper version, which was first proposed in 1977. Conclusion-blind review, proposed by Robin Hanson in 2007 extends this further asking all authors to submit a positive and a negative version, and only after the journal has accepted the article authors reveal which is the real version. Pre-accepted articles or outcome-unbiased journals or advance publication review or registered reports or prior to results submission or early acceptance extends study pre-registration to the point that journals accepted or reject papers based on the version of the paper written before the results or conclusions have been made (an enlarged study protocol), but instead describes the theoretical justification, experimental design, and statistical analysis. Only once the proposed hypothesis and methodology have been accepted by reviewers, the authors would collect the data or analyze previously collected data. A limited variant of a pre-accepted article was The Lancet's study protocol review from 1997 to 2015 reviewed and published randomized trial protocols with


id: scholarly-peer-review-20260505-7
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-7.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review has been subject to several criticisms, and various proposals for reforming the system have been suggested over the years. Many studies have emphasized the problems inherent to the process of peer review. Moreover, Ragone et al. have shown that there is a low correlation between peer review outcomes and the future impact measured by citations. Various biomedical editors in particular have expressed criticism of peer review. A Cochrane review found little empirical evidence that peer review ensures quality in biomedical research, while a second systematic review and meta-analysis found a need for evidence-based peer review in biomedicine given the paucity of assessment of the interventions designed to improve the process. To an outsider, the anonymous, pre-publication peer review process is opaque. Certain journals are accused of not carrying out stringent peer review in order to more easily expand their customer base, particularly in journals where authors pay a fee before publication. Richard Smith, MD, former editor of the British Medical Journal, has claimed that peer review is "ineffective, largely a lottery, anti-innovatory, slow, expensive, wasteful of scientific time, inefficient, easily abused, prone to bias, unable to detect fraud and irrelevant; Several studies have shown that peer review


id: scholarly-peer-review-20260505-8
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-8.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Peer review and trust === Researchers have peer-reviewed manuscripts prior to publishing them in a variety of ways since the 18th century. The main goal of this practice is to improve the relevance and accuracy of scientific discussions. Even though experts often criticize peer review for a number of reasons, the process is still often considered the "gold standard" of science. Occasionally however, peer review approves studies that are later found to be wrong and rarely deceptive or fraudulent results are discovered prior to publication. Thus, there seems to be an element of discord between the ideology behind and the practice of peer review. By failing to effectively communicate that peer review is imperfect, the message conveyed to the wider public is that studies published in peer-reviewed journals are "true" and that peer review protects the literature from flawed science. A number of well-established criticisms exist of many elements of peer review. In the following we describe cases of the wider impact inappropriate peer review can have on public understanding of scientific literature. Multiple examples across several areas of science find that scientists elevated the importance of peer review for research that was questionable or corrupted. For example, climate


id: scholarly-peer-review-20260505-9
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-9.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
The belief that scholars are incapable of evaluating the quality of work on their own, that they are in need of a gatekeeper to inform them of what is good and what is not. The belief that scholars need a "guardian" to make sure they are doing good work. Others argue that authors most of all have a vested interest in the quality of a particular piece of work. Only the authors could have, as Feynman (1974) puts it, the "extra type of integrity that is beyond not lying, but bending over backwards to show how you're maybe wrong, that you ought to have when acting as a scientist." If anything, the current peer review process and academic system could penalize, or at least fail to incentivize, such integrity. Instead, the credibility conferred by the "peer-reviewed" label could diminish what Feynman calls the culture of doubt necessary for science to operate a self-correcting, truth-seeking process. The effects of this can be seen in the ongoing replication crisis, hoaxes, and widespread outrage over the inefficacy of the current system. It's common to think that more oversight is the answer, as peer reviewers are not at all lacking in skepticism. But the


id: scholarly-peer-review-20260505-10
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-10.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Allegations of bias and suppression === The interposition of editors and reviewers between authors and readers may enable the intermediators to act as gatekeepers. Some sociologists of science argue that peer review makes the ability to publish susceptible to control by elites and to personal jealousy. The peer review process may sometimes impede progress and may be biased against novelty. A linguistic analysis of review reports suggests that reviewers focus on rejecting the applications by searching for weak points, and not on finding the high-risk/high-gain groundbreaking ideas that may be in the proposal. Reviewers tend to be especially critical of conclusions that contradict their own views, and lenient towards those that match them. At the same time, established scientists are more likely than others to be sought out as referees, particularly by high-prestige journals/publishers. As a result, ideas that harmonize with the established experts' are more likely to see print and to appear in premier journals than are iconoclastic or revolutionary ones. This accords with Thomas Kuhn's well-known observations regarding scientific revolutions. A theoretical model has been established whose simulations imply that peer review and over-competitive research funding foster mainstream opinion to monopoly. Criticisms of traditional anonymous peer review


id: scholarly-peer-review-20260505-11
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-11.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
==== Plagiarism ==== Reviewers generally lack access to raw data, but do see the full text of the manuscript, and are typically familiar with recent publications in the area. Thus, they are in a better position to detect plagiarism of prose than fraudulent data. A few cases of such textual plagiarism by historians, for instance, have been widely publicized. On the scientific side, a poll of 3,247 scientists funded by the U.S. National Institutes of Health found 0.3% admitted faking data and 1.4% admitted plagiarism. Additionally, 4.7% of the same poll admitted to self-plagiarism or autoplagiarism, in which an author republishes the same material, data, or text, without citing their earlier work. Self-plagiarisms are less likely to be detected in double-blinded peer reviews. ==== Examples ==== "Perhaps the most widely recognized failure of peer review is its inability to ensure the identification of high-quality work. The list of important scientific papers that were rejected by some peer-reviewed journals goes back at least as far as the editor of Philosophical Transaction's 1796 rejection of Edward Jenner's report of the first vaccination against smallpox." The Soon and Baliunas controversy involved the publication in 2003 of a review study written by aerospace engineer


id: sham-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Sham_peer_review-0.md
category: reference
title: Sham peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sham_peer_review
date_saved: 2026-05-05T09:53:19.440577+00:00
instance: kb-cron
parent: sham-peer-review-20260505-0
summary:
Sham peer review or malicious peer review is a name given to the abuse of a medical peer review process to attack a doctor for personal or other non-medical reasons. The American Medical Association conducted an investigation of medical peer review in 2007 and concluded that while it is easy to allege misconduct and 15% of surveyed physicians indicated that they were aware of peer review misuse or abuse, cases of malicious peer review able to be proven through the legal system are rare. == Legal basis == Those who maintain that sham peer review is a pervasive problem suggest that the Healthcare Quality Improvement Act (HCQIA) of 1986 allows sham reviews by granting significant immunity from liability to doctors and others who participate in peer reviews. This immunity extends to investigative activities as well as to any associated peer review hearing, whether or not it leads to a disciplinary (or other) action. The definition of a peer review body can be broad, including not only individuals but also (for example, in Oregon), "tissue committees, governing bodies or committees including medical staff committees of a [licensed] health care facility...or any other medical group in connection with bona fide medical research,


id: sham-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Sham_peer_review-1.md
category: reference
title: Sham peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sham_peer_review
date_saved: 2026-05-05T09:53:19.440577+00:00
instance: kb-cron
parent: sham-peer-review-20260505-0
summary:
=== Roland Chalifoux === Roland Chalifoux, member of an advocacy organisation called the Semmelweis Society, had his medical license revoked in Texas in 2004 after numerous incidents including the death of a patient. The Texas State Board of Medical Examiners stated that Chalifoux's practices "constitute such a deviation from the standard of care that revocation of his license is the only sanction that will adequately protect the public". Chalifoux subsequently secured permission to practice in West Virginia, and alleges that the Texas board's actions constitute sham peer review. === Charles Williams, MD === Six years after Charles Williams, MD, an anesthesiologist was summarily suspended by University Medical Center of Southern Nevada, a federal jury in Las Vegas awarded Dr. Williams $8.8 million as compensation for the due process violations he experienced in his sham peer review. Before the trial, which began May 16, U.S. District Judge Philip Pro made a finding that Ellerton and UMC's medical staff had violated Williams' due process rights. That left only the question of damages for the jury. This case appears to be the highest jury verdict in the nation for sham peer review which has not been overturned. === Richard Chudacoff, MD === On


id: software-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Software_peer_review-0.md
category: reference
title: Software peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Software_peer_review
date_saved: 2026-05-05T09:53:20.598380+00:00
instance: kb-cron
parent: software-peer-review-20260505-0
summary:
In software development, peer review is a type of software review in which a work product (document, code, or other) is examined by the author's colleagues, in order to evaluate the work product's technical content and quality. == Purpose == The purpose of a peer review is to provide "a disciplined engineering practice for detecting and correcting defects in software artifacts, and preventing their leakage into field operations" according to the Capability Maturity Model. When performed as part of each Software development process activity, peer reviews identify problems that can be fixed early in the lifecycle. That is to say, a peer review that identifies a requirements problem during the Requirements analysis activity is cheaper and easier to fix than during the Software architecture or Software testing activities. The National Software Quality Experiment, evaluating the effectiveness of peer reviews, finds, "a favorable return on investment for software inspections; savings exceeds costs by 4 to 1". To state it another way, it is four times more costly, on average, to identify and fix a software problem later. == Distinction from other types of software review == Peer reviews are distinct from management reviews, which are conducted by management representatives rather than


id: sternberg-peer-review-controversy-20260505-0
file: data/en.wikipedia.org/wiki/Sternberg_peer_review_controversy-0.md
category: reference
title: Sternberg peer review controversy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sternberg_peer_review_controversy
date_saved: 2026-05-05T09:53:21.772833+00:00
instance: kb-cron
parent: sternberg-peer-review-controversy-20260505-0
summary:
The Sternberg peer review controversy concerns the conflict arising from the publication of an article supporting pseudoscientific intelligent design creationism in a scientific journal, and the subsequent questions of whether proper editorial procedures had been followed and whether it was properly peer reviewed. On 4 August 2004, an article by Stephen C. Meyer (Director of Discovery Institute's Center for Science and Culture) titled "The origin of biological information and the higher taxonomic categories", appeared in the peer-reviewed journal, Proceedings of the Biological Society of Washington. Meyer's article was a literature review article, and contained no new primary scholarship itself on the topic of intelligent design. The following month, the publisher of the journal, the Council of the Biological Society of Washington, released a statement repudiating the article and stating that their former editor, biologist Richard M. Sternberg, handled the entire review process in an unusual manner, without consultation or review from an associate editor. The position of editor was unpaid and voluntary, and Sternberg had put in his resignation from it six months earlier. Sternberg disputes the Council's statement and asserts that the paper was appropriately peer reviewed by three biologists who "concluded that [the paper] warranted publication". The same


id: sternberg-peer-review-controversy-20260505-1
file: data/en.wikipedia.org/wiki/Sternberg_peer_review_controversy-1.md
category: reference
title: Sternberg peer review controversy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sternberg_peer_review_controversy
date_saved: 2026-05-05T09:53:21.772833+00:00
instance: kb-cron
parent: sternberg-peer-review-controversy-20260505-0
summary:
=== Richard M. Sternberg === Richard M. Sternberg is an American evolutionary biologist who has completed a BS degree from University of South Carolina and has two PhDs: the first from 1995 in molecular evolution from Florida International University, and a second in systems science from Binghamton University. He did post-doctoral work between 1999 and 2001 at the National Museum of Natural History (NMNH) at the Smithsonian Institution, on the phylogeny of crabs. In February 2001 he began work as an invertebrate taxonomist for the National Institutes of Health at the National Center for Biotechnology Information, later becoming a staff scientist. The position allowed time for research work, and he continued at the NMNH, now as an unpaid research associate. Soon after starting work, he agreed to take on the unpaid position of Managing Editor of the peer-reviewed scientific journal, Proceedings of the Biological Society of Washington, a taxonomic journal which usually publishes descriptions of newly identified species. Also in 2001, he joined the editorial board of the Baraminology Study Group, a young earth creationist "creation science" attempt to identify and classify the created kinds mentioned in scripture. He has stated that he is an outside critic and remained skeptical


id: sternberg-peer-review-controversy-20260505-2
file: data/en.wikipedia.org/wiki/Sternberg_peer_review_controversy-2.md
category: reference
title: Sternberg peer review controversy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sternberg_peer_review_controversy
date_saved: 2026-05-05T09:53:21.772833+00:00
instance: kb-cron
parent: sternberg-peer-review-controversy-20260505-0
summary:
Doubts were raised whether the reviewers were evolutionary biologists. According to an article by the Society of Academic Authors Meyer said the article grew out of a presentation he made at a conference attended by Richard Sternberg where they discussed the possibility of a paper for society's journal. Observers have pointed to affiliations that in most circumstances would have disqualified Sternberg from reviewing an article on intelligent design. They note that Sternberg is a Fellow of the International Society for Complexity, Information and Design, a Discovery Institute-affiliated group dedicated to promoting intelligent design. Sternberg is also a signatory of the Discovery Institute's A Scientific Dissent From Darwinism statement "We are skeptical of claims for the ability of random mutation and natural selection to account for the complexity of life. Careful examination of the evidence for Darwinian theory should be encouraged." Sternberg claims to have also checked with a Council member and to have followed the standard practice for peer review: Three reviewers responded and were willing to review the paper; all are experts in relevant aspects of evolutionary and molecular biology and hold full-time faculty positions in major research institutions, one at an Ivy League university, another at a major


id: sternberg-peer-review-controversy-20260505-3
file: data/en.wikipedia.org/wiki/Sternberg_peer_review_controversy-3.md
category: reference
title: Sternberg peer review controversy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sternberg_peer_review_controversy
date_saved: 2026-05-05T09:53:21.772833+00:00
instance: kb-cron
parent: sternberg-peer-review-controversy-20260505-0
summary:
== Smithsonian controversy == After the peer review controversy became public, Sternberg filed a religious discrimination complaint against the Smithsonian Institution's National Museum of Natural History, where he had an unpaid appointment as a research associate, while employed by the National Institutes of Health. Sternberg claims that he was "targeted for retaliation and harassment" and subject to efforts to remove him from the museum in retaliation for his views in support of intelligent design. He continues to cite a letter by the United States Office of Special Counsel as supporting his version of events, despite the fact that the Office of Special Counsel did not proceed beyond its initial investigation. In its initial investigation, Office of Special Counsel concluded that there was a strategy by several managers to force Sternberg out of the Smithsonian Institution and that there was a strong religious and political component to the actions taken after the publication of the Meyer article. For example, one of the emails sent between Smithsonian employees stated, "Scientists have been perfectly willing to let these people alone in their churches, but now it looks like these people are coming out and invading our schools, biology classes, museums and now our


id: technical-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Technical_peer_review-0.md
category: reference
title: Technical peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Technical_peer_review
date_saved: 2026-05-05T09:53:22.988147+00:00
instance: kb-cron
parent: technical-peer-review-20260505-0
summary:
In engineering, technical peer review is a well defined review process for finding and correcting defects conducted by a team of peers with assigned roles. Technical peer reviews are carried out by peers representing areas of life cycle affected by material being reviewed (usually limited to 6 or fewer people). Technical peer reviews are held within development phases, between milestone reviews, on completed products, or on completed portions of products. A technical peer review may also be called an engineering peer review, a product peer review, a peer review/inspection or an inspection. == Overview == The purpose of a technical peer review is to remove defects as early as possible in the development process. By removing defects at their origin (e.g., requirements and design documents, test plans and procedures, software code, etc.), technical peer reviews prevent defects from propagating through multiple phases and work products and reduce the overall amount of rework necessary on projects. Improved team efficiency is a side effect (e.g., by improving team communication, integrating the viewpoints of various engineering specialty disciplines, more quickly bringing new members up to speed, and educating project members about effective development practices). In CMMI, peer reviews are used as a principal


id: u-s-government-peer-review-policies-20260505-0
file: data/en.wikipedia.org/wiki/U.S._Government_peer_review_policies-0.md
category: reference
title: U.S. Government peer review policies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/U.S._Government_peer_review_policies
date_saved: 2026-05-05T09:53:24.170660+00:00
instance: kb-cron
parent: u-s-government-peer-review-policies-20260505-0
summary:
Most federal regulatory agencies in the United States government must comply with specific peer review requirements before the agencies publicly disseminate certain scientific information. These requirements were published in a Peer Review Bulletin issued by the White House Office of Management and Budget (OMB), which establishes "government-wide standards concerning when peer review is required and, if required, what type of peer review processes are appropriate." == Bulletin == OMB's peer review bulletin requires that US federal regulatory agencies submit all "influential scientific information" to peer review before the information is publicly disseminated. The Bulletin defines "scientific information" as: factual inputs, data, models, analyses, technical information, or scientific assessments related to such disciplines as the behavioral and social sciences, public health and medical sciences, life and earth sciences, engineering, or physical sciences. This Bulletin defines "influential scientific information" as scientific information the agency reasonably can determine will have or does have a clear and substantial impact on important public policies or private sector decisions. In the term 'influential scientific information,' the term 'influential' should be interpreted consistently with OMB's government-wide information quality guidelines and the information quality guidelines of the agency. As noted in the preceding quotation, the Bulletin must be


id: united-states-office-of-research-integrity-20260505-0
file: data/en.wikipedia.org/wiki/United_States_Office_of_Research_Integrity-0.md
category: reference
title: United States Office of Research Integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/United_States_Office_of_Research_Integrity
date_saved: 2026-05-05T09:53:25.437954+00:00
instance: kb-cron
parent: united-states-office-of-research-integrity-20260505-0
summary:
The Office of Research Integrity (ORI) is a U.S. government agency that focuses on research integrity, especially in health. It was created when the Office of Scientific Integrity (OSI) in the National Institutes of Health (NIH) and the Office of Scientific Integrity Review (OSIR) in the Office of the Assistant Secretary for Health merged in May 1992. The Office of Research Integrity oversees and directs Public Health Service (PHS) research integrity activities on behalf of the Secretary of Health and Human Services, except for the regulatory research integrity activities of the Food and Drug Administration. Organizationally, ORI is located within the Office of the Assistant Secretary for Health (OASH) within the Office of the Secretary of Health and Human Services (OS), in the Department of Health and Human Services (HHS). == Activities == ORI carries out its responsibility by: Developing policies, procedures and regulations related to the detection, investigation, and prevention of research misconduct and the responsible conduct of research; Reviewing and monitoring research misconduct investigations conducted by applicant and awardee institutions, intramural research programs, and the Office of Inspector General in the Department of Health and Human Services (HHS); Recommending research misconduct findings and administrative actions to the Assistant


id: who-s-afraid-of-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Who's_Afraid_of_Peer_Review-0.md
category: reference
title: Who's Afraid of Peer Review?
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Who's_Afraid_of_Peer_Review?
date_saved: 2026-05-05T09:53:26.673104+00:00
instance: kb-cron
parent: who-s-afraid-of-peer-review-20260505-0
summary:
"Who's Afraid of Peer Review?" is an article written by Science correspondent John Bohannon that describes his investigation of peer review among fee-charging open-access journals. Between January and August 2013, Bohannon submitted fake scientific papers to 304 journals owned by fee-charging open access publishers. The papers, writes Bohannon, "were designed with such grave and obvious scientific flaws that they should have been rejected immediately by editors and peer reviewers", but 60% of the journals accepted them. The article and associated data were published in the 4 October 2013 issue of Science as open access. == Background == The first fee-charging open access scientific journals began appearing in 2000 with the creation of BioMed Central and then the Public Library of Science. Rather than deriving at least some of their revenue from subscription fees, fee-charging open access journals only charge the authors (or their funders) a publication fee. The published papers are then freely available on the internet. This business model, gold open access, is one of several solutions devised to make open access publishing sustainable. The number of articles published open access, or made freely available after some time behind a paywall (delayed open access), has grown rapidly. A 2013


id: who-s-afraid-of-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Who's_Afraid_of_Peer_Review-1.md
category: reference
title: Who's Afraid of Peer Review?
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Who's_Afraid_of_Peer_Review?
date_saved: 2026-05-05T09:53:26.673104+00:00
instance: kb-cron
parent: who-s-afraid-of-peer-review-20260505-0
summary:
=== Responses from open-access academic publishers === Since the story was released, publishers of three journals have stated that they are shutting them down. The DOAJ is reviewing its list and instituting tighter criteria for inclusion. The Open Access Scholarly Publishers Association (OASPA) formed a committee to investigate the circumstances that led to the acceptance of the fake paper by three of its members. On 11 November 2013, OASPA terminated the membership of two publishers (Dove Medical Press and Hikari Ltd.) who accepted the fake paper. SAGE Publications, which also accepted a fake paper, was put "under review" for 6 months. Sage announced in a statement that it was reviewing the journal that accepted the fake paper, but that it would not shut it down. Sage's membership was reinstated at the end of the review period following changes to the journal's editorial processes. === Responses from the scientific community === Within hours of its publication, the Science investigation came under intense criticism by some supporters of the open-access movement. The first substantial critique was posted by PLOS cofounder Michael Eisen on his blog. "To suggest – as Science (though not Bohannon) are trying to do – that the problem with


id: writers-workshop-activity-20260505-0
file: data/en.wikipedia.org/wiki/Writers_workshop_(activity)-0.md
category: reference
title: Writers workshop (activity)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Writers_workshop_(activity)
date_saved: 2026-05-05T09:53:27.785028+00:00
instance: kb-cron
parent: writers-workshop-activity-20260505-0
summary:
A writing workshop is a group session where writers gather to share, critique and improve their work. Various models of writing workshops have been developed over time to suit different educational settings and writing goals. Workshop attendance might be restricted to a select group (such as a writers' circle or class) or open to the public. == Workshop models == Most workshops include some kind of sharing of work, reflection upon and discussion of the work, and often suggestions for improvement. There may or may not be a facilitator, who is usually an established writer or teacher, but can be a volunteer from among the group. Workshops can work on very established rules and formats, or can be more freeform. === Traditional workshop model === The traditional workshop model, also known as the Iowa model, was developed at the University of Iowa Writers' Workshop in the mid-20th century, under the directorship of Paul Engle. It typically involves: Submission: Participants submit their writing in advance. Each participant is expected to read everyone else's work and come with feedback. Critique: During the workshop, participants take turns to provide feedback on each person's work in turn. When it's their turn to receive group


id: data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Data_analysis-0.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays an important role in making decisions more scientific and helping businesses operate more effectively. It is widely used in fields such as business analytics, healthcare, and artificial intelligence to extract meaningful insights from data. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data, while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a


id: data-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Data_analysis-1.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
Mathematical formulas or mathematical models (supported by algorithms) may be applied to the data in order to identify relationships among the variables; for example, checking for correlation and by determining whether or not there is the presence of causality. In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model's accuracy (e.g., Data = Model + Error). Inferential statistics utilizes techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y), i.e. is Y a function of X? This can be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X. === Data product === A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about


id: data-analysis-20260505-2
file: data/en.wikipedia.org/wiki/Data_analysis-2.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
McKinsey and Company named a technique for breaking down a quantitative problem into its component parts called the MECE principle. MECE means "Mutually Exclusive and Collectively Exhaustive". Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them. For example, profit by definition can be broken down into total revenue and total cost. Analysts may use robust statistical measurements to solve certain analytical problems. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that hypothesis is true or false. For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept called the Phillips Curve. Hypothesis testing involves considering the likelihood of Type I and type II errors, which relate to whether the data supports accepting or rejecting the hypothesis. Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate


id: data-analysis-20260505-3
file: data/en.wikipedia.org/wiki/Data_analysis-3.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
=== Initial data analysis === The most important distinction between the initial data analysis phase and the main analysis phase is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions: ==== Quality of data ==== The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), normal imputation is needed. Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution. Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable. Test for common-method variance. The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase. ==== Quality of measurements ==== The quality of the measurement instruments should only be checked during the initial data


id: data-analysis-20260505-4
file: data/en.wikipedia.org/wiki/Data_analysis-4.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
==== Exploratory and confirmatory approaches ==== In the main analysis phase, either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis, clear hypotheses about the data are tested. Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted


id: causal-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Causal_analysis-0.md
category: reference
title: Causal analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causal_analysis
date_saved: 2026-05-05T09:53:30.564050+00:00
instance: kb-cron
parent: causal-analysis-20260505-0
summary:
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more controlled or natural experiments. == Motivation == Data analysis is primarily concerned with causal questions. For example, did the fertilizer cause the crops to grow? Or, can a given sickness be prevented? Or, why is my friend depressed? The potential outcomes and regression analysis techniques handle such queries when data is collected using designed experiments. Data collected in observational studies require different techniques for causal inference (because, for example, of issues such as confounding). Causal inference techniques used with experimental data require additional assumptions to produce reasonable inferences with observation data. The difficulty of causal inference under such circumstances is often summed up as "correlation does not imply causation". == In philosophy and physics == The nature of causality is systematically investigated in several academic disciplines, including philosophy and physics. In academia,


id: ads-txt-20260505-0
file: data/en.wikipedia.org/wiki/Ads.txt-0.md
category: reference
title: Ads.txt
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ads.txt
date_saved: 2026-05-05T09:53:31.690614+00:00
instance: kb-cron
parent: ads-txt-20260505-0
summary:
ads.txt (Authorized Digital Sellers) is an initiative from IAB Technology Laboratory. It specifies a text file that companies can host on their web servers, listing the other companies authorized to sell their products or services. This is designed to allow online buyers to check the validity of the sellers from whom they buy, for the purposes of internet fraud prevention. == State of adoption == By November 2017, more than 44% of publishers had ads.txt files. More than 90,000 sites were using ads.txt, up from 3,500 in September 2017, according to Pixalate. Among the top 1,000 sites that sold programmatic ads, 57 percent had ads.txt files, compared to 16 percent in September, per Pixalate. Latest adoption data per FirstImpression.io's Ads.txt Industry Dashboard: Google has been an active proponent of ads.txt. From the end of October 2017 Google Display & Video 360 only buys inventory from sources identified as authorized sellers in a publisher's ads.txt file, when a file is available. ads.txt may become a requirement for Display & Video 360. == File format == The IAB's ads.txt specification dictates the formatting of ads.txt files, which can contain three types of record; data records, variables and comments. An ads.txt file can


id: augmented-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Augmented_Analytics-0.md
category: reference
title: Augmented Analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Augmented_Analytics
date_saved: 2026-05-05T09:53:32.830056+00:00
instance: kb-cron
parent: augmented-analytics-20260505-0
summary:
Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. Augmented analytics is based on business intelligence and analytics. In the graph extraction step, data from different sources are investigated. == Defining Augmented Analytics == Machine Learning – a systematic computing method that uses algorithms to sift through data to identify relationships, trends, and patterns. It is a process that allows algorithms to dynamically learn from data instead of having a set base of programmed rules. Natural language generation (NLG) – a software capability that takes unstructured data and translates it into plain-English, readable, language. Automating Insights – using machine learning algorithms to automate data analysis processes. Natural Language Query – enabling users to query data using business terms that are either typed onto a search box or spoken. == Data Democratization == Data Democratization is the democratizing data access in order to relieve data congestion and get rid of any sense of data "gatekeepers". This process must be implemented


id: badir-20260505-0
file: data/en.wikipedia.org/wiki/BADIR-0.md
category: reference
title: BADIR
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/BADIR
date_saved: 2026-05-05T09:53:34.122536+00:00
instance: kb-cron
parent: badir-20260505-0
summary:
The BADIR (pronounced /ˈbaːdɪr/) is a structured data science and data analytics process designed to enhance data-driven decision-making within organizations by addressing both analytical output as well as usefulness to management. It was developed by Piyanka Jain and Puneet Sharma and first published in the 2014 book “Behind Every Good Decision”. == Overview == The BADIR Framework employs a hypothesis-driven approach that involves understanding business objectives and challenges, analysis planning before acquiring and ensuring the quality of relevant data, applying analytics to derive insights and the potential impact on the business challenge, developing actionable recommendations aligned with strategic goals, and implementing decisions while monitoring outcomes and making necessary adjustments. == Key Components of BADIR == The BADIR Framework consists of interrelated components essential for decision-making. Its main assertion is that if data analytics does not drive business impact, then it is just statistics, not analytics. The acronym in the framework stands for the following five steps: B = Business Question: The first step in the framework is to define the real business question. Market trends, customer feedback, competitor actions, etc. are data sources commonly used by businesses. However, these data alone do not help teams to make decisions. A =


id: base-effect-20260505-0
file: data/en.wikipedia.org/wiki/Base_effect-0.md
category: reference
title: Base effect
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Base_effect
date_saved: 2026-05-05T09:53:35.330547+00:00
instance: kb-cron
parent: base-effect-20260505-0
summary:
The base effect is a mathematical effect that originates from the fact that a given percentage of a reference value, is not the same as the absolute difference of the same given percentage of a much larger or smaller reference value. E.g. 1% of a GDP of US$1 million is not equal to 1% of GDP of US$1 billion in terms of absolute difference. The reference value is common called a base year in economics. A low base effect is the tendency of an absolute change from a low initial amount to be translated into a larger percentage change, while a high base effect would be the tendency of an absolute change from a high initial amount to be translated into a smaller percentage change. Because of the base effect percentages in time series analysis can be misleading, in particular when percentages are compounded annually over a period of many years. A high base effect can mislead because of decreasing percentages even while the underlying absolute difference is increasing over time as much as the measured value or population increases over time. Also, a low base effect can mislead because of increasing percentages even while the absolute difference is decreasing


id: big-data-20260505-0
file: data/en.wikipedia.org/wiki/Big_data-0.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offers greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sources. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data that have only volume, velocity, and variety can pose challenges in sampling. A fourth concept, veracity, which refers to the level of reliability of data, was thus added. Without sufficient investment in expertise to ensure big data veracity, the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture value from big data. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now


id: big-data-20260505-1
file: data/en.wikipedia.org/wiki/Big_data-1.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
Volume The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not. The size of big data is usually larger than terabytes and petabytes. Variety The type and nature of the data. Earlier technologies like RDBMSs were capable to handle structured data efficiently and effectively. However, the change in type and nature from structured to semi-structured or unstructured challenged the existing tools and technologies. Big data technologies evolved with the prime intention to capture, store, and process the semi-structured and unstructured (variety) data generated with high speed (velocity), and huge in size (volume). Later, these tools and technologies were explored and used for handling structured data also but preferable for storage. Eventually, the processing of structured data was still kept as optional, either using big data or traditional RDBMSs. This helps in analyzing data towards effective usage of the hidden insights exposed from the data collected via social media, log files, sensors, etc. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion. Velocity The speed at which the data is generated and processed to meet the


id: big-data-20260505-2
file: data/en.wikipedia.org/wiki/Big_data-2.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
== Architecture == Big data repositories have existed in many forms, often built by corporations with a special need. Commercial vendors historically offered parallel database management systems for big data beginning in the 1990s. For many years, WinterCorp published the largest database report. Teradata Corporation in 1984 marketed the parallel processing DBC 1012 system. Teradata systems were the first to store and analyze 1 terabyte of data in 1992. Hard disk drives were 2.5 GB in 1991 so the definition of big data continuously evolves. Teradata installed the first petabyte class RDBMS based system in 2007. As of 2017, there are a few dozen petabyte class Teradata relational databases installed, the largest of which exceeds 50 PB. Systems up until 2008 were 100% structured relational data. Since then, Teradata has added semi structured data types including XML, JSON, and Avro. In 2000, Seisint Inc. (now LexisNexis Risk Solutions) developed a C++-based distributed platform for data processing and querying known as the HPCC Systems platform. This system automatically partitions, distributes, stores and delivers structured, semi-structured, and unstructured data across multiple commodity servers. Users can write data processing pipelines and queries in a declarative dataflow programming language called ECL. Data analysts working


id: big-data-20260505-3
file: data/en.wikipedia.org/wiki/Big_data-3.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
Big data has increased the demand of information management specialists so much so that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP, and Dell have spent more than $15 billion on software firms specializing in data management and analytics. In 2010, this industry was worth more than $100 billion and was growing at almost 10 percent a year, about twice as fast as the software business as a whole. Developed economies increasingly use data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide, and between 1 billion and 2 billion people accessing the internet. Between 1990 and 2005, more than 1 billion people worldwide entered the middle class, which means more people became more literate, which in turn led to information growth. The world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007 and predictions put the amount of internet traffic at 667 exabytes annually by 2014. According to one estimate, one-third of the globally stored information is in the form of alphanumeric text and still image data, which is the format most useful for most big data applications. This also shows the potential


id: big-data-20260505-4
file: data/en.wikipedia.org/wiki/Big_data-4.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Healthcare === Big data analytics has been used in healthcare in providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries. Some areas of improvement are more aspirational than actually implemented. The level of data generated within healthcare systems is not trivial. With the added adoption of mHealth, eHealth and wearable technologies the volume of data will continue to increase. This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data. There is now an even greater need for such environments to pay greater attention to data and information quality. "Big data very often means 'dirty data' and the fraction of data inaccuracies increases with data volume growth." Human inspection at the big data scale is impossible and there is a desperate need in health service for intelligent tools for accuracy and believability control and handling of information missed. While extensive information in healthcare is now electronic, it fits under the big data umbrella as most is unstructured and difficult to use. The use of big data in


id: big-data-20260505-5
file: data/en.wikipedia.org/wiki/Big_data-5.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Survey science === Compared to survey-based data collection, big data has low cost per data point, applies analysis techniques via machine learning and data mining, and includes diverse and new data sources, e.g., registers, social media, apps, and other forms digital data. Since 2018, survey scientists have started to examine how big data and survey science can complement each other to allow researchers and practitioners to improve the production of statistics and its quality. There have been three Big Data Meets Survey Science (BigSurv) conferences in 2018, 2020 (virtual), 2023, and as of 2023 one conference forthcoming in 2025, a special issue in the Social Science Computer Review, a special issue in Journal of the Royal Statistical Society, and a special issue in EP J Data Science, and a book called Big Data Meets Social Sciences edited by Craig Hill and five other Fellows of the American Statistical Association. In 2021, the founding members of BigSurv received the Warren J. Mitofsky Innovators Award from the American Association for Public Opinion Research. === Marketing === Big data is notable in marketing due to the constant "datafication" of everyday consumers of the internet, in which all forms of data are tracked.


id: big-data-20260505-6
file: data/en.wikipedia.org/wiki/Big_data-6.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Science === The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.99995% of these streams, there are 1,000 collisions of interest per second. As a result, only working with less than 0.001% of the sensor stream data, the data flow from all four LHC experiments represents 25 petabytes annual rate before replication (as of 2012). This becomes nearly 200 petabytes after replication. If all sensor data were recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed 150 million petabytes annual rate, or nearly 500 exabytes per day, before replication. To put the number in perspective, this is equivalent to 500 quintillion (5×1020) bytes per day, almost 200 times more than all the other sources combined in the world. The Square Kilometre Array is a radio telescope built of thousands of antennas. It is expected to be operational by 2024. Collectively, these antennas are expected to gather 14 exabytes and store one petabyte per day. It is considered one of the most ambitious scientific projects ever undertaken.


id: big-data-20260505-7
file: data/en.wikipedia.org/wiki/Big_data-7.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
== Research activities == Encrypted search and cluster formation in big data were demonstrated in March 2014 at the American Society of Engineering Education. Gautam Siwach engaged at Tackling the challenges of Big Data by MIT Computer Science and Artificial Intelligence Laboratory and Amir Esmailpour at the UNH Research Group investigated the key features of big data as the formation of clusters and their interconnections. They focused on the security of big data and the orientation of the term towards the presence of different types of data in an encrypted form at cloud interface by providing the raw definitions and real-time examples within the technology. Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data. In March 2012, The White House announced a national "Big Data Initiative" that consisted of six federal departments and agencies committing more than $200 million to big data research projects. The initiative included a National Science Foundation "Expeditions in Computing" grant of $10 million over five years to the AMPLab at the University of California, Berkeley. The AMPLab also received funds from DARPA, and over a dozen industrial


id: big-data-20260505-8
file: data/en.wikipedia.org/wiki/Big_data-8.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Critiques of the big data paradigm === "A crucial problem is that we do not know much about the underlying empirical micro-processes that lead to the emergence of the[se] typical network characteristics of Big Data." In their critique, Snijders, Matzat, and Reips point out that often very strong assumptions are made about mathematical properties that may not at all reflect what is really going on at the level of micro-processes. Mark Graham has leveled broad critiques at Chris Anderson's assertion that big data will spell the end of theory: focusing in particular on the notion that big data must always be contextualized in their social, economic, and political contexts. Even as companies invest eight- and nine-figure sums to derive insight from information streaming in from suppliers and customers, less than 40% of employees have sufficiently mature processes and skills to do so. To overcome this insight deficit, big data, no matter how comprehensive or well analyzed, must be complemented by "big judgment", according to an article in the Harvard Business Review. Much in the same line, it has been pointed out that the decisions based on the analysis of big data are inevitably "informed by the world as it


id: big-data-20260505-9
file: data/en.wikipedia.org/wiki/Big_data-9.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Critiques of big data execution === Ulf-Dietrich Reips and Uwe Matzat wrote in 2014 that big data had become a "fad" in scientific research. Researcher danah boyd has raised concerns about the use of big data in science neglecting principles such as choosing a representative sample by being too concerned about handling the huge amounts of data. This approach may lead to results that have a bias in one way or another. Integration across heterogeneous data resources—some that might be considered big data and others not—presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science. In the provocative article "Critical Questions for Big Data", the authors title big data a part of mythology: "large data sets offer a higher form of intelligence and knowledge [...], with the aura of truth, objectivity, and accuracy". Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth". Recent developments in BI domain, such as pro-active reporting especially target improvements in the usability


id: collocation-remote-sensing-20260505-0
file: data/en.wikipedia.org/wiki/Collocation_(remote_sensing)-0.md
category: reference
title: Collocation (remote sensing)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Collocation_(remote_sensing)
date_saved: 2026-05-05T09:53:37.734675+00:00
instance: kb-cron
parent: collocation-remote-sensing-20260505-0
summary:
Collocation is a procedure used in remote sensing to match measurements from two or more different instruments. This is done for two main reasons: for validation purposes when comparing measurements of the same variable, and to relate measurements of two different variables either for performing retrievals or for prediction. In the second case the data is later fed into some type of statistical inverse method such as an artificial neural network, statistical classification algorithm, kernel estimator or a linear least squares. In principle, most collocation problems can be solved by a nearest neighbor search, but in practice there are many other considerations involved and the best method is highly specific to the particular matching of instruments. Here we deal with some of the most important considerations along with specific examples. There are at least two main considerations when performing collocations. The first is the sampling pattern of the instrument. Measurements may be dense and regular, such as those from a cross-track scanning satellite instrument. In this case, some form of interpolation may be appropriate. On the other hand, the measurements may be sparse, such as a one-off field campaign designed for some particular validation exercise. The second consideration is the


id: collocation-remote-sensing-20260505-1
file: data/en.wikipedia.org/wiki/Collocation_(remote_sensing)-1.md
category: reference
title: Collocation (remote sensing)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Collocation_(remote_sensing)
date_saved: 2026-05-05T09:53:37.734675+00:00
instance: kb-cron
parent: collocation-remote-sensing-20260505-0
summary:
== Interpolation == For gridded data such as assimilation or reanalysis data, interpolation is likely the most appropriate method for performing any type of comparison. A specific point in both physical position and time is easy to locate within the grid and interpolation performed between the nearest neighbors. Linear interpolation (bilinear, trilinear etc.) is the most common, though cubic is used as well but is probably not worth the extra computational overhead. If the variable of interest has a relatively smooth rate of change (temperature is a good example of this because it has a diffusion mechanism, radiative transfer, not available to other atmospheric variables), then interpolation can eliminate much of the error associated with collocation. Interpolation may also be appropriate for many types of satellite instruments, for instance a cross-track scanning instrument like Landsat. In data derived from the Advanced Microwave Sounding Unit (AMSU) are interpolated (although not for the purposes of collocation) using a slight variation of trilinear interpolation. Since measurements within a single scan track are laid out in an approximately rectangular grid, bilinear interpolation can be performed. By searching for the nearest overlapping scan track both forwards and backwards in time, the spatial interpolates can then


id: combinatorial-data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Combinatorial_data_analysis-0.md
category: reference
title: Combinatorial data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Combinatorial_data_analysis
date_saved: 2026-05-05T09:53:38.930340+00:00
instance: kb-cron
parent: combinatorial-data-analysis-20260505-0
summary:
In statistics, combinatorial data analysis (CDA) is the study of data sets where the order in which objects are arranged is important. CDA can be used either to determine how well a given combinatorial construct reflects the observed data, or to search for a suitable combinatorial construct that does fit the data. == See also == Cluster analysis Geometric data analysis Structured data analysis (statistics) Seriation (statistics) == References ==


id: concept-drift-20260505-0
file: data/en.wikipedia.org/wiki/Concept_drift-0.md
category: reference
title: Concept drift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Concept_drift
date_saved: 2026-05-05T09:53:40.159460+00:00
instance: kb-cron
parent: concept-drift-20260505-0
summary:
In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. Drift detection and drift adaptation are of paramount importance in the fields that involve dynamically changing data and data models. == Predictive model decay == In machine learning and predictive analytics this drift phenomenon is called concept drift. In machine learning, a common element of a data model are the statistical properties, such as probability distribution of the actual data. If they deviate from the statistical properties of the training data set, then the learned predictions may become invalid, if the drift is not addressed. == Data configuration decay == Another important area is software engineering, where three types of data drift affecting data fidelity may be recognized. Changes in the software environment ("infrastructure drift") may invalidate software infrastructure configuration. "Structural drift" happens when the data schema changes, which may invalidate databases. "Semantic drift" is changes in the meaning of data


id: concept-drift-20260505-1
file: data/en.wikipedia.org/wiki/Concept_drift-1.md
category: reference
title: Concept drift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Concept_drift
date_saved: 2026-05-05T09:53:40.159460+00:00
instance: kb-cron
parent: concept-drift-20260505-0
summary:
=== Remedy methods === DDM (Drift Detection Method): detects drift by monitoring the model's error rate over time. When the error rate passes a set threshold, it enters a warning phase, and if it passes another threshold, it enters a drift phase. EDDM (Early Drift Detection Method): improves DDM's detection rate by tracking the average distance between two errors instead of only the error rate. ADWIN (Adaptive Windowing): dynamically stores a window of recent data and warns the user if it detects a significant change between the statistics of the window's earlier data compared to more recent data. KSWIN (Kolmogorov–Smirnov Windowing): detects drift based on the Kolmogorov-Smirnov statistical test. DDM and EDDM: Concept Drift Detection online supervised methods that rely on sequential error monitoring to estimate the evolving error rate. ADWIN and KSWIN: Windowing maintain a "window", a subset of the most recent data, of the data stream, which it checks for statistical differences across the window. == Applications in security == Concept drift is a recurring issue in security analytics, especially in malware and intrusion detection. In these systems, models are often trained on past logs, binaries or network traces, but the behaviour of attackers changes over time as


id: concept-drift-20260505-2
file: data/en.wikipedia.org/wiki/Concept_drift-2.md
category: reference
title: Concept drift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Concept_drift
date_saved: 2026-05-05T09:53:40.159460+00:00
instance: kb-cron
parent: concept-drift-20260505-0
summary:
=== Concept Drift, Concept Evolution, and Adversarial Manipulation === Concept drift refers to when the relationship between input features and their corresponding labels changes gradually over time. Consider a simple example: phishing emails. The older kinds of attacks would leverage prominent signals, such as the presence of the word "lottery," while newer variants would introduce more polished phrasing, such as "lottery and not scam," in attempts to appear more legitimate and evade detection. This is a form of concept drift because the underlying class—phishing—remains the same, but the feature patterns that define it shift as attackers adapt. It is important, though, to distinguish concept drift from its related but inherently different challenge: concept evolution.“Concept evolution” occurs when an entirely new attack family emerges with feature patterns the model has never encountered. The model is exposed to new features and patterns that were not present in the training data. In the multi-class setting, it means a completely new label is introduced. For the binary setting (benign vs malicious), a completely new malicious family is introduced; however, the model still tries to make predictions based on outdated feature patterns. One such example would be if a model is trained with both trojan


id: concept-drift-20260505-3
file: data/en.wikipedia.org/wiki/Concept_drift-3.md
category: reference
title: Concept drift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Concept_drift
date_saved: 2026-05-05T09:53:40.159460+00:00
instance: kb-cron
parent: concept-drift-20260505-0
summary:
==== Real ==== USP Data Stream Repository, 27 real-world stream datasets with concept drift compiled by Souza et al. (2020). Access Airline, approximately 116 million flight arrival and departure records (cleaned and sorted) compiled by E. Ikonomovska. Reference: Data Expo 2009 Competition [1]. Access Chess.com (online games) and Luxembourg (social survey) datasets compiled by I. Zliobaite. Access ECUE spam 2 datasets each consisting of more than 10,000 emails collected over a period of approximately 2 years by an individual. Access from S.J.Delany webpage Elec2, electricity demand, 2 classes, 45,312 instances. Reference: M. Harries, Splice-2 comparative evaluation: Electricity pricing, Technical report, The University of South Wales, 1999. Access from J.Gama webpage. Comment on applicability. PAKDD'09 competition data represents the credit evaluation task. It is collected over a five-year period. Unfortunately, the true labels are released only for the first part of the data. Access Sensor stream and Power supply stream datasets are available from X. Zhu's Stream Data Mining Repository. Access SMEAR is a benchmark data stream with a lot of missing values. Environment observation data over 7 years. Predict cloudiness. Access Text mining, a collection of text mining datasets with concept drift, maintained by I. Katakis. Access Gas Sensor Array


id: continuous-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Continuous_analytics-0.md
category: reference
title: Continuous analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Continuous_analytics
date_saved: 2026-05-05T09:53:41.378945+00:00
instance: kb-cron
parent: continuous-analytics-20260505-0
summary:
Continuous analytics is a data science process that abandons ETLs and complex batch data pipelines in favor of cloud-native and microservices paradigms. Continuous data processing enables real time interactions and immediate insights with fewer resources. == Defined == Analytics is the application of mathematics and statistics to big data. Data scientists write analytics programs to look for solutions to business problems, like forecasting demand or setting an optimal price. The continuous approach runs multiple stateless engines which concurrently enrich, aggregate, infer and act on the data. Data scientists, dashboards and client apps all access the same raw or real-time data derivatives with proper identity-based security, data masking and versioning in real-time. Traditionally, data scientists have not been part of IT development teams, like regular Java programmers. This is because their skills set them apart in their own department not normally related to IT, i.e., math, statistics, and data science. So it is logical to conclude that their approach to writing software code does not enjoy the same efficiencies as the traditional programming team. In particular traditional programming has adopted the Continuous Delivery approach to writing code and the agile methodology. That releases software in a continuous circle, called iterations. Continuous


id: cosine-similarity-20260505-0
file: data/en.wikipedia.org/wiki/Cosine_similarity-0.md
category: reference
title: Cosine similarity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cosine_similarity
date_saved: 2026-05-05T09:53:42.619881+00:00
instance: kb-cron
parent: cosine-similarity-20260505-0
summary:
In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval [ − 1 , + 1 ] . {\displaystyle [-1,+1].} For example, two proportional vectors have a cosine similarity of +1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of −1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in [ 0 , 1 ] {\displaystyle [0,1]} . For example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and


id: cosine-similarity-20260505-1
file: data/en.wikipedia.org/wiki/Cosine_similarity-1.md
category: reference
title: Cosine similarity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cosine_similarity
date_saved: 2026-05-05T09:53:42.619881+00:00
instance: kb-cron
parent: cosine-similarity-20260505-0
summary:
angular distance = D θ := arccos ⁡ ( cosine similarity ) π = θ π {\displaystyle {\text{angular distance}}=D_{\theta }:={\frac {\arccos({\text{cosine similarity}})}{\pi }}={\frac {\theta }{\pi }}} angular similarity = S θ := 1 − angular distance = 1 − θ π {\displaystyle {\text{angular similarity}}=S_{\theta }:=1-{\text{angular distance}}=1-{\frac {\theta }{\pi }}} Or, if the vector elements are always positive: angular distance = D θ := 2 ⋅ arccos ⁡ ( cosine similarity ) π = 2 θ π {\displaystyle {\text{angular distance}}=D_{\theta }:={\frac {2\cdot \arccos({\text{cosine similarity}})}{\pi }}={\frac {2\theta }{\pi }}} angular similarity = S θ := 1 − angular distance = 1 − 2 θ π {\displaystyle {\text{angular similarity}}=S_{\theta }:=1-{\text{angular distance}}=1-{\frac {2\theta }{\pi }}} Unfortunately, computing the inverse cosine (arccos) function is slow, making the use of the angular distance more computationally expensive than using the more common (but not metric) cosine distance above. === L2-normalized Euclidean distance === Another effective proxy for cosine distance can be obtained by L 2 {\displaystyle L_{2}} normalisation of the vectors, followed by the application of normal Euclidean distance. Using this technique each term in each vector is first divided by the magnitude of the vector, yielding a vector of unit length. Then the Euclidean distance over


id: cosine-similarity-20260505-2
file: data/en.wikipedia.org/wiki/Cosine_similarity-2.md
category: reference
title: Cosine similarity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cosine_similarity
date_saved: 2026-05-05T09:53:42.619881+00:00
instance: kb-cron
parent: cosine-similarity-20260505-0
summary:
cos ⁡ ( ∠ A C − ∠ C B ) ≥ cos ⁡ ( ∠ A B ) ≥ cos ⁡ ( ∠ A C + ∠ C B ) . {\displaystyle \cos(\angle {AC}-\angle {CB})\geq \cos(\angle {AB})\geq \cos(\angle {AC}+\angle {CB}).} Using the cosine addition and subtraction formulas, these two inequalities can be written in terms of the original cosines, cos ⁡ ( A , C ) ⋅ cos ⁡ ( C , B ) + ( 1 − cos ⁡ ( A , C ) 2 ) ⋅ ( 1 − cos ⁡ ( C , B ) 2 ) ≥ cos ⁡ ( A , B ) , {\displaystyle \cos(A,C)\cdot \cos(C,B)+{\sqrt {\left(1-\cos(A,C)^{2}\right)\cdot \left(1-\cos(C,B)^{2}\right)}}\geq \cos(A,B),} cos ⁡ ( A , B ) ≥ cos ⁡ ( A , C ) ⋅ cos ⁡ ( C , B ) − ( 1 − cos ⁡ ( A , C ) 2 ) ⋅ ( 1 − cos ⁡ ( C , B ) 2 ) . {\displaystyle \cos(A,B)\geq \cos(A,C)\cdot \cos(C,B)-{\sqrt {\left(1-\cos(A,C)^{2}\right)\cdot \left(1-\cos(C,B)^{2}\right)}}.} This form of the triangle inequality can be used to bound the minimum and maximum similarity of two objects A and B if the similarities to a reference


id: cross-industry-standard-process-for-data-mining-20260505-0
file: data/en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining-0.md
category: reference
title: Cross-industry standard process for data mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
date_saved: 2026-05-05T09:53:43.884915+00:00
instance: kb-cron
parent: cross-industry-standard-process-for-data-mining-20260505-0
summary:
The Cross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model. In 2015, IBM released a new methodology called Analytics Solutions Unified Method for Data Mining/Predictive Analytics (also known as ASUM-DM), which refines and extends CRISP-DM. == History == CRISP-DM was conceived in 1996 and became a European Union project under the ESPRIT funding initiative in 1997. The project was led by five companies: Integral Solutions Ltd (ISL), Teradata, Daimler AG, NCR Corporation, and OHRA, an insurance company. This core consortium brought different experiences to the project. ISL, was later acquired and merged into SPSS. The computer giant NCR Corporation produced the Teradata data warehouse and its own data mining software. Daimler-Benz had a significant data mining team. OHRA was starting to explore the potential use of data mining. The first version of the methodology was presented at the 4th CRISP-DM SIG Workshop in Brussels in March 1999, and published as a step-by-step data mining guide later that year. Between 2006 and 2008, a CRISP-DM 2.0 SIG was formed, and there were discussions about updating the CRISP-DM process model.


id: dark-data-20260505-0
file: data/en.wikipedia.org/wiki/Dark_data-0.md
category: reference
title: Dark data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Dark_data
date_saved: 2026-05-05T09:53:45.047576+00:00
instance: kb-cron
parent: dark-data-20260505-0
summary:
Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the data. In some cases the organisation may not even be aware that the data is being collected. IBM estimate that roughly 90 percent of data generated by sensors and analog-to-digital conversions never get used. In an industrial context, dark data can include information gathered by sensors and telematics. Organizations retain dark data for a multitude of reasons, and it is estimated that most companies are only analyzing 1% of their data. Often it is stored for regulatory compliance and record keeping. Some organizations believe that dark data could be useful to them in the future, once they have acquired better analytic and business intelligence technology to process the information. Because storage is inexpensive, storing data is easy. However, storing and securing the data usually entails greater expenses (or even risk) than the potential return profit. In academic discourse, the term dark data was essentially coined by Bryan P. Heidorn. He uses it to describe research data, especially


id: data-access-20260505-0
file: data/en.wikipedia.org/wiki/Data_access-0.md
category: reference
title: Data access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_access
date_saved: 2026-05-05T09:53:46.267466+00:00
instance: kb-cron
parent: data-access-20260505-0
summary:
Data access is a generic term referring to a process which has both an IT-specific meaning and other connotations involving access rights in a broader legal and/or political sense. In the former it typically refers to software and activities related to storing, retrieving, or acting on data housed in a database or other repository. == Details == Two fundamental types of data access exist: sequential access (as in magnetic tape, for example) random access (as in indexed media) Data access crucially involves authorization to access different data repositories. Data access can help distinguish the abilities of administrators and users. For example, administrators may have the ability to remove, edit and add data, while general users may not even have "read" rights if they lack access to particular information. Historically, each repository (including each different database, file system, etc.), might require the use of different methods and languages, and many of these repositories stored their content in different and incompatible formats. Over the years standardized languages, methods, and formats, have developed to serve as interfaces between the often proprietary, and always idiosyncratic, specific languages and methods. Such standards include SQL (1974- ), ODBC (ca 1990- ), JDBC, XQJ, ADO.NET, XML, XQuery,


id: data-analysis-for-fraud-detection-20260505-0
file: data/en.wikipedia.org/wiki/Data_analysis_for_fraud_detection-0.md
category: reference
title: Data analysis for fraud detection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis_for_fraud_detection
date_saved: 2026-05-05T09:53:47.474137+00:00
instance: kb-cron
parent: data-analysis-for-fraud-detection-20260505-0
summary:
Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes. In general, the primary reason to use data analytics techniques is to tackle fraud since many internal control systems have serious weaknesses. For example, the currently prevailing approach employed by many law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of fraud cases remain undetected and unprosecuted. In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations rely on specialized data analytics techniques such as data mining, data matching, the sounds like function, regression analysis, clustering analysis, and gap analysis. Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence. == Statistical techniques == Examples of statistical data analysis techniques are: Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data. Calculation


id: data-analysis-for-fraud-detection-20260505-1
file: data/en.wikipedia.org/wiki/Data_analysis_for_fraud_detection-1.md
category: reference
title: Data analysis for fraud detection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis_for_fraud_detection
date_saved: 2026-05-05T09:53:47.474137+00:00
instance: kb-cron
parent: data-analysis-for-fraud-detection-20260505-0
summary:
Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations and can help to get better insights into the processes behind the data. Although the traditional data analysis techniques can indirectly lead us to knowledge, it is still created by human analysts. To go beyond, a data analysis system has to be equipped with a substantial amount of background knowledge, and be able to perform reasoning tasks involving that knowledge and the data provided. In effort to meet this goal, researchers have turned to ideas from the machine learning field. This is a natural source of ideas, since the machine learning task can be described as turning background knowledge and examples (input) into knowledge (output). If data mining results in discovering meaningful patterns, data turns into information. Information or patterns that are novel, valid and potentially useful are not merely information, but knowledge. One speaks of discovering knowledge, before hidden in the huge amount of data, but now revealed. The machine learning and artificial intelligence solutions may be classified into two categories: 'supervised' and 'unsupervised' learning. These methods seek for accounts, customers, suppliers, etc. that behave 'unusually' in order to output


id: data-exploration-20260505-0
file: data/en.wikipedia.org/wiki/Data_exploration-0.md
category: reference
title: Data exploration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_exploration
date_saved: 2026-05-05T09:53:48.638463+00:00
instance: kb-cron
parent: data-exploration-20260505-0
summary:
Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data. Data exploration is typically conducted using a combination of automated and manual activities. Automated activities can include data profiling or data visualization or tabular reports to give the analyst an initial view into the data and an understanding of key characteristics. This is often followed by manual drill-down or filtering of the data to identify anomalies or patterns identified through the automated actions. Data exploration can also require manual scripting and queries into the data (e.g. using languages such as SQL or R) or using spreadsheets or similar tools to view the raw data. All of these activities are aimed at creating a mental model and understanding of the data in the mind of the analyst, and defining basic metadata (statistics, structure, relationships) for the data set that can be used in further analysis. Once this


id: data-farming-20260505-0
file: data/en.wikipedia.org/wiki/Data_farming-0.md
category: reference
title: Data farming
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_farming
date_saved: 2026-05-05T09:53:49.876847+00:00
instance: kb-cron
parent: data-farming-20260505-0
summary:
Data farming is the process of using designed computational experiments to “grow” data, which can then be analyzed using statistical and visualization techniques to obtain insight into complex systems. These methods can be applied to any computational model. Data farming differs from Data mining, as the following metaphors indicate: Miners seek valuable nuggets of ore buried in the earth, but have no control over what is out there or how hard it is to extract the nuggets from their surroundings. ... Similarly, data miners seek to uncover valuable nuggets of information buried within massive amounts of data. Data-mining techniques use statistical and graphical measures to try to identify interesting correlations or clusters in the data set. Farmers cultivate the land to maximize their yield. They manipulate the environment to their advantage using irrigation, pest control, crop rotation, fertilizer, and more. Small-scale designed experiments let them determine whether these treatments are effective. Similarly, data farmers manipulate simulation models to their advantage, using large-scale designed experimentation to grow data from their models in a manner that easily lets them extract useful information. ...the results can reveal root cause-and-effect relationships between the model input factors and the model responses, in addition to rich


id: data-fusion-20260505-0
file: data/en.wikipedia.org/wiki/Data_fusion-0.md
category: reference
title: Data fusion
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_fusion
date_saved: 2026-05-05T09:53:51.162435+00:00
instance: kb-cron
parent: data-fusion-20260505-0
summary:
Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place. Low-level data fusion combines several sources of raw data to produce new raw data. The expectation is that fused data is more informative and synthetic than the original inputs. For example, sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion. The concept of data fusion has origins in the evolved capacity of humans and animals to incorporate information from multiple senses to improve their ability to survive. For example, a combination of sight, touch, smell, and taste may indicate whether a substance is edible. == The JDL/DFIG model == In the mid-1980s, the Joint Directors of Laboratories formed the Data Fusion Subpanel (which later became known as the Data Fusion Group). With the advent of the World Wide Web, data fusion thus included data, sensor, and information fusion. The JDL/DFIG introduced a model of data fusion that divided the various processes. Currently, the six levels with


id: data-fusion-20260505-1
file: data/en.wikipedia.org/wiki/Data_fusion-1.md
category: reference
title: Data fusion
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_fusion
date_saved: 2026-05-05T09:53:51.162435+00:00
instance: kb-cron
parent: data-fusion-20260505-0
summary:
== For enhanced contextual awareness == With a multitude of built-in sensors including motion sensor, environmental sensor, position sensor, a modern mobile device typically gives mobile applications access to a number of sensory data which could be leveraged to enhance the contextual awareness. Using signal processing and data fusion techniques such as feature generation, feasibility study and principal component analysis (PCA) such sensory data will greatly improve the positive rate of classifying the motion and contextual relevant status of the device. Many context-enhanced information techniques are provided by Snidaro, et al. == Statistical methods == === Bayesian auto-regressive Gaussian processes === Gaussian processes are a popular machine learning model. If an auto-regressive relationship between the data is assumed, and each data source is assumed to be a Gaussian process, this constitutes a non-linear Bayesian regression problem. === Semiparametric estimation === Many data fusion methods assume common conditional distributions across several data sources. Recently, methods have been developed to enable efficient estimation within the resulting semiparametric model. == See also == Data assimilation Data munging Image fusion Information integration Integrative level Meta-analysis Sensor fusion == References == === Sources === General references Hall, Dave L.; Llinas, James (1997). "Introduction to Multisensor


id: data-profiling-20260505-0
file: data/en.wikipedia.org/wiki/Data_profiling-0.md
category: reference
title: Data profiling
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_profiling
date_saved: 2026-05-05T09:53:52.402806+00:00
instance: kb-cron
parent: data-profiling-20260505-0
summary:
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. The purpose of these statistics may be to: Find out whether existing data can be easily used for other purposes Improve the ability to search data by tagging it with keywords, descriptions, or assigning it to a category Assess data quality, including whether the data conforms to particular standards or patterns Assess the risk involved in integrating data in new applications, including the challenges of joins Discover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependencies Assess whether known metadata accurately describes the actual values in the source database Understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns. Have an enterprise view of all data, for uses such as master data management, where key data is needed, or data governance for improving data quality. == Introduction == Data profiling refers to the analysis of information for use in a data warehouse


id: data-science-20260505-0
file: data/en.wikipedia.org/wiki/Data_science-0.md
category: reference
title: Data science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_science
date_saved: 2026-05-05T09:53:53.540719+00:00
instance: kb-cron
parent: data-science-20260505-0
summary:
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms, and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data. Data science plays a critical role in modern decision-making by enabling organizations to extract actionable insights from large and complex datasets. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is distinct from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. Data science is often described as a multidisciplinary and rapidly


id: data-science-20260505-1
file: data/en.wikipedia.org/wiki/Data_science-1.md
category: reference
title: Data science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_science
date_saved: 2026-05-05T09:53:53.540719+00:00
instance: kb-cron
parent: data-science-20260505-0
summary:
data collection and integration; data cleaning and preparation (handling missing values, outliers, encoding, normalisation); feature engineering and selection; visualisation and descriptive statistics; fitting and evaluating statistical or machine-learning models; communicating results and ensuring reproducibility (e.g., reports, notebooks, and dashboards). Lifecycle frameworks such as CRISP-DM describe these steps from business understanding through deployment and monitoring. Data science involves working with larger datasets that often require advanced computational and statistical methods to analyze. Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models. Data science often uses statistical analysis, data preprocessing, and supervised learning. Recent studies indicate that AI is moving towards data-centric approaches, focusing on the quality of datasets rather than just improving AI models. This trend focuses on improving system performance by cleaning, refining, and labeling data (Bhatt et al., 2024). As AI systems grow larger, the data-centric view has become increasingly important. == Cloud computing for data science == Cloud computing can offer access to large amounts of computational power and storage. In big data, where volumes of information are continually generated and processed, these platforms can be used to handle complex and resource-intensive analytical tasks. Some distributed


id: data-version-control-20260505-0
file: data/en.wikipedia.org/wiki/Data_version_control-0.md
category: reference
title: Data version control
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_version_control
date_saved: 2026-05-05T09:53:54.761385+00:00
instance: kb-cron
parent: data-version-control-20260505-0
summary:
Data version control is a method of working with data sets. It is similar to the version control systems used in traditional software development, but is optimized to allow better processing of data and collaboration in the context of data analytics, research, and any other form of data analysis. Data version control may also include specific features and configurations designed to facilitate work with large data sets and data lakes. == History == === Background === As early as 1985, researchers recognized the need for defining timing attributes in database tables, which would be necessary for tracking changes to databases. This research continued into the 1990s, and the theory was formalized into practical methods for managing data in relational databases, providing some of the foundational concepts for what would later become data version control. In the early 2010s the size of data sets was rapidly expanding, and relational databases were no longer sufficient to manage the amounts of data organizations were accumulating. The rise of the Apache Hadoop eco system, with HDFS as a storage layer, and later object storage had become dominant in big data operations. Research into data management tools and data version control systems increased sharply, along


id: data-driven-model-20260505-0
file: data/en.wikipedia.org/wiki/Data-driven_model-0.md
category: reference
title: Data-driven model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data-driven_model
date_saved: 2026-05-05T09:53:55.946685+00:00
instance: kb-cron
parent: data-driven-model-20260505-0
summary:
Data-driven models are a class of computational models that primarily rely on historical data collected throughout a system's or process' lifetime to establish relationships between input, internal, and output variables. Commonly found in numerous articles and publications, data-driven models have evolved from earlier statistical models, overcoming limitations posed by strict assumptions about probability distributions. These models have gained prominence across various fields, particularly in the era of big data, artificial intelligence, and machine learning, where they offer valuable insights and predictions based on the available data. == Background == These models have evolved from earlier statistical models, which were based on certain assumptions about probability distributions that often proved to be overly restrictive. The emergence of data-driven models in the 1950s and 1960s coincided with the development of digital computers, advancements in artificial intelligence research, and the introduction of new approaches in non-behavioural modelling, such as pattern recognition and automatic classification. == Key Concepts == Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, neural networks for approximating functions, global optimization and evolutionary computing, statistical learning theory, and Bayesian


id: data-informed-decision-making-20260505-0
file: data/en.wikipedia.org/wiki/Data-informed_decision-making-0.md
category: reference
title: Data-informed decision-making
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data-informed_decision-making
date_saved: 2026-05-05T09:53:57.178692+00:00
instance: kb-cron
parent: data-informed-decision-making-20260505-0
summary:
Data-informed decision-making (DIDM) refers to the collection and analysis of data to guide decisions and improve chances of success. Another form of this process is referred to as data-driven decision-making, "which is defined similarly as making decisions based on hard data as opposed to intuition, observation, or guesswork." DIDM is used in education communities, where data is used with the goal of helping students and improving curricula, among other fields in which data is used to inform decisions. While "data based decision-making" is a more common term, "data-informed decision-making" is the preferred term, since decisions should not be based solely on quantitative data. Data-driven decision-making is commonly used in the context of business growth and entrepreneurship. Many educators have access to some type of a data system for analyzing their students' data. These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system, making key package/display and content decisions) to improve the success of educators' data-informed decision-making. In business, fostering and actively supporting data-driven decision-making in their firm and among their colleagues may be one of the central responsibilities of CIOs (Chief Information Officers) or CDOs (Chief Data Officers). Assessment in


id: dataset-shift-20260505-0
file: data/en.wikipedia.org/wiki/Dataset_shift-0.md
category: reference
title: Dataset shift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Dataset_shift
date_saved: 2026-05-05T09:53:59.453907+00:00
instance: kb-cron
parent: dataset-shift-20260505-0
summary:
Dataset shift is a phenomenon in machine learning and statistics in which the joint distribution of input variables and target labels is different in the training phase and the deployment or test phase (i.e., P t r a i n ( X , Y ) ≠ P t e s t ( X , Y ) {\displaystyle P_{train}(X,Y)\neq P_{test}(X,Y)} ). This happens when the statistical properties of data used to train a model are no longer representative of the data encountered in real-world use, often resulting in degraded predictive performance and diminished generalization ability. Dataset shift is a generic term for a number of particular types of distributional change. Covariate shift is when the distribution of the input features changes, but the conditional relationship between inputs and outputs remains constant . Prior probability shift (or label shift) happens when the distribution of target labels changes, but the conditional distribution of inputs given labels stays the same. Concept shift (also known as concept drift) is the change of the conditional relationship between inputs and outputs that renders previously learned patterns invalid over time. A key challenge for deploying machine learning systems is dataset shift, in particular in dynamic environments where the


id: degree-rips-bifiltration-20260505-0
file: data/en.wikipedia.org/wiki/Degree-Rips_bifiltration-0.md
category: reference
title: Degree-Rips bifiltration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Degree-Rips_bifiltration
date_saved: 2026-05-05T09:54:00.644610+00:00
instance: kb-cron
parent: degree-rips-bifiltration-20260505-0
summary:
The degree-Rips bifiltration is a simplicial filtration used in topological data analysis for analyzing the shape of point cloud data. It is a multiparameter extension of the Vietoris–Rips filtration that possesses greater stability to data outliers than single-parameter filtrations, and which is more amenable to practical computation than other multiparameter constructions. Introduced in 2015 by Lesnick and Wright, the degree-Rips bifiltration is a parameter-free and density-sensitive vehicle for performing persistent homology computations on point cloud data. == Definition == It is standard practice in topological data analysis (TDA) to associate a sequence of nested simplicial complexes to a finite data set in order to detect the persistence of topological features over a range of scale parameters. One way to do this is by considering the sequence of Vietoris–Rips complexes of a finite set in a metric space indexed over all scale parameters. If X {\displaystyle X} is a finite set in a metric space, then this construction is known as the Vietoris–Rips (or simply "Rips") filtration on X {\displaystyle X} , commonly denoted Rips ( X ) {\displaystyle {\text{Rips}}(X)} or R ( X ) {\displaystyle {\mathcal {R}}(X)} . The Rips filtration can be expressed as a functor Rips ( X


id: directional-component-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Directional_component_analysis-0.md
category: reference
title: Directional component analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Directional_component_analysis
date_saved: 2026-05-05T09:54:01.826255+00:00
instance: kb-cron
parent: directional-component-analysis-20260505-0
summary:
Directional component analysis (DCA) is a statistical method used in climate science for identifying representative patterns of variability in space-time data-sets such as historical climate observations, weather prediction ensembles or climate ensembles. The first DCA pattern is a pattern of weather or climate variability that is both likely to occur (measured using likelihood) and has a large impact (for a specified linear impact function, and given certain mathematical conditions: see below). The first DCA pattern contrasts with the first PCA pattern, which is likely to occur, but may not have a large impact, and with a pattern derived from the gradient of the impact function, which has a large impact, but may not be likely to occur. DCA differs from other pattern identification methods used in climate research, such as EOFs, rotated EOFs and extended EOFs in that it takes into account an external vector, the gradient of the impact. DCA provides a way to reduce large ensembles from weather forecasts or climate models to just two patterns. The first pattern is the ensemble mean, and the second pattern is the DCA pattern, which represents variability around the ensemble mean in a way that takes impact into account. DCA contrasts


id: directional-component-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Directional_component_analysis-1.md
category: reference
title: Directional component analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Directional_component_analysis
date_saved: 2026-05-05T09:54:01.826255+00:00
instance: kb-cron
parent: directional-component-analysis-20260505-0
summary:
The two axes represent anomalies of annual mean rainfall at two locations, with the highest total rainfall anomaly values towards the top right corner of the diagram The joint variability of the rainfall anomalies at the two locations is assumed to follow a bivariate normal distribution The ellipse shows a single contour of probability density from this bivariate normal, with higher values inside the ellipse The red dot at the centre of the ellipse shows zero rainfall anomalies at both locations The blue parallel-line arrow shows the principal axis of the ellipse, which is also the first PCA spatial pattern vector In this case, the PCA pattern is scaled so that it touches the ellipse The diagonal straight line shows a line of constant positive total rainfall anomaly, assumed to be at some fairly extreme level The red dotted-line arrow shows the first DCA pattern, which points towards the point at which the diagonal line is tangent to the ellipse In this case, the DCA pattern is scaled so that it touches the ellipse From this diagram, the DCA pattern can be seen to possess the following properties: Of all the points on the diagonal line, it is the one


id: directional-change-intrinsic-time-20260505-0
file: data/en.wikipedia.org/wiki/Directional-change_intrinsic_time-0.md
category: reference
title: Directional-change intrinsic time
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Directional-change_intrinsic_time
date_saved: 2026-05-05T09:54:03.000900+00:00
instance: kb-cron
parent: directional-change-intrinsic-time-20260505-0
summary:
Directional-change intrinsic time is an event-based operator to dissect a data series into a sequence of alternating trends of defined size δ {\displaystyle \delta } . The directional-change intrinsic time operator was developed for the analysis of financial market data series. It is an alternative methodology to the concept of continuous time. Directional-change intrinsic time operator dissects a data series into a set of drawups and drawdowns or up and down trends that alternate with each other. An established trend comes to an end as soon as a trend reversal is observed. A price move that extends a trend is called overshoot and leads to new price extremes. Figure 1 provides an example of a price curve dissected by the directional change intrinsic time operator. The frequency of directional-change intrinsic events maps (1) the volatility of price changes conditional to (2) the selected threshold δ {\displaystyle \delta } . The stochastic nature of the underlying process is mirrored in the non-equal number of intrinsic events observed over equal periods of physical time. Directional-change intrinsic time operator is a noise filtering technique. It identifies regime shifts, when trend changes of a particular size occur and hides price fluctuations that are smaller


id: doacross-parallelism-20260505-0
file: data/en.wikipedia.org/wiki/DOACROSS_parallelism-0.md
category: reference
title: DOACROSS parallelism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/DOACROSS_parallelism
date_saved: 2026-05-05T09:54:04.174776+00:00
instance: kb-cron
parent: doacross-parallelism-20260505-0
summary:
DOACROSS parallelism is a parallelization technique used to perform Loop-level parallelism by utilizing synchronisation primitives between statements in a loop. This technique is used when a loop cannot be fully parallelized by DOALL parallelism due to data dependencies between loop iterations, typically loop-carried dependencies. The sections of the loop which contain loop-carried dependence are synchronized, while treating each section as a parallel task on its own. Therefore, DOACROSS parallelism can be used to complement DOALL parallelism to reduce loop execution times. == Description == DOACROSS parallelism is particularly useful when one statement depends on the values generated by another statement. In such a loop, DOALL parallelism can not be implemented in a straightforward manner. If the first statement blocks the execution of the second statement until the required value has been produced, then the two statements would be able to execute independent of each other (i.e.), each of the aforementioned statements would be parallelized for simultaneous execution using DOALL parallelism. The following pseudocode illustrates the operation of DOACROSS parallelism in such a situation. === Example === In this example, each iteration of the loop requires the value written into a by the previous iteration. However, the entire statement is not


id: document-oriented-database-20260505-0
file: data/en.wikipedia.org/wiki/Document-oriented_database-0.md
category: reference
title: Document-oriented database
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Document-oriented_database
date_saved: 2026-05-05T09:54:05.454091+00:00
instance: kb-cron
parent: document-oriented-database-20260505-0
summary:
A document-oriented database, or document store, is a computer program and data storage system designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data. Document-oriented databases are one of the main categories of NoSQL databases, and the popularity of the term "document-oriented database" has grown alongside the adoption of NoSQL itself. XML databases are a subclass of document-oriented databases optimized for XML documents. Graph databases are similar, but add another layer, the relationship, which allows them to link documents for rapid traversal. Document-oriented databases are conceptually an extension of the key–value store, another type of NoSQL database. In key-value stores, data is treated as opaque by the database, whereas document-oriented systems exploit the internal structure of documents to extract metadata and optimize storage and queries. Although in practice the distinction can be minimal due to modern tooling, document stores are designed to provide a richer programming experience with modern programming techniques. Document databases differ significantly from traditional relational databases (RDBs). Relational databases store data in predefined tables, often requiring an object to be split across multiple tables. In contrast, document databases store all information for a given object in a single document, with each document potentially having


id: document-oriented-database-20260505-1
file: data/en.wikipedia.org/wiki/Document-oriented_database-1.md
category: reference
title: Document-oriented database
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Document-oriented_database
date_saved: 2026-05-05T09:54:05.454091+00:00
instance: kb-cron
parent: document-oriented-database-20260505-0
summary:
=== Relationship to relational databases === In a relational database, data is organized into predefined types represented as tables. Each table contains rows (records) with a fixed set of columns (fields), so all records in a table share the same structure. Administrators typically define indexes on selected fields to improve query performance. A central principle of relational database design is database normalization, in which data that might otherwise be repeated is stored in separate tables and linked using keys. When records in different tables are related, a foreign key is used to associate them. For example, an address book application may store a contact's name, image, phone numbers, mailing addresses, and email addresses. In a normalized relational design, separate tables might be created for contacts, phone numbers, and email addresses. The phone number table would include a foreign key referencing the associated contact. To reconstruct a complete contact record, the database retrieves related information from each table using the foreign keys and combines it into a single record. In contrast, a document-oriented database stores all data related to an object within a single document, and stored in the database as a single entry. In the address book example,the contact's name,


id: doe-mean-plot-20260505-0
file: data/en.wikipedia.org/wiki/DOE_mean_plot-0.md
category: reference
title: DOE mean plot
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/DOE_mean_plot
date_saved: 2026-05-05T09:54:06.621514+00:00
instance: kb-cron
parent: doe-mean-plot-20260505-0
summary:
In statistics, specifically the analysis of data in the design of experiments, a DOE mean plot is a graphical tool used to analyze data from an experiment. In it, it demonstrates the relative importance of all the factors in an experiment with respect to a chosen location statistic, in this case the mean. Each factor is plotted and all mean levels of the factor (two or more) are connected with straight lines. The plot is meant to complement other analyses, such as the typical analysis of variance. == Example == Shown below is an example from Box, Hunter, and Hunter (1978). In it, several factors are chosen to determine their effect(s) on bike speed. These factors are: seat height, dynamo engaged (yes/no), angle of the handlebar, chosen gear, raincoat (on/off), breakfast (yes/no), and tire pressure (low/high), each at two levels. The plot shows the mean response level for each variable (vertical) versus the factor (horizontal). To interpret the graph, all that is needed is to determine the ordering of the factors by length of the line connecting the two dots. The longer the line, the more significant the factor. Therefore, this plot clearly demonstrates that factor 4 (chosen gear) is


id: double-mass-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Double_mass_analysis-0.md
category: reference
title: Double mass analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Double_mass_analysis
date_saved: 2026-05-05T09:54:07.787564+00:00
instance: kb-cron
parent: double-mass-analysis-20260505-0
summary:
Double mass analysis is a simple graphical method to evaluate the consistency of hydrological data. The DM approach plots the cumulative data of one variable against the cumulative data of a second variable. A break in the slope of a linear function fit to the data is thought to represent a change in the relation between the variables. This approach provides a robust method to determine a change in the behavior of precipitation and recharge in a simple graphical method. It is a commonly used data analysis approach for investigating the behaviour of records made of hydrological or meteorological data at a number of locations. It is used to determine whether there is a need for corrections to the data - to account for changes in data collection procedures or other local conditions. Such changes may result from a variety of things including changes in instrumentation, changes in observation procedures, or changes in gauge location or surrounding conditions. Double mass analysis for checking consistency of a hydrological or meteorological record is considered to be an essential tool before taking it for analysis purpose. This method is based on the hypothesis that each item of the recorded data of a population


id: error-level-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Error_level_analysis-0.md
category: reference
title: Error level analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Error_level_analysis
date_saved: 2026-05-05T09:54:08.944694+00:00
instance: kb-cron
parent: error-level-analysis-20260505-0
summary:
Error level analysis (ELA) is the analysis of compression artifacts in digital data with lossy compression such as JPEG. == Principles == When used, lossy compression is normally applied uniformly to a set of data, such as an image, resulting in a uniform level of compression artifacts. Alternatively, the data may consist of parts with different levels of compression artifacts. This difference may arise from the different parts having been repeatedly subjected to the same lossy compression a different number of times, or the different parts having been subjected to different kinds of lossy compression. A difference in the level of compression artifacts in different parts of the data may therefore indicate that the data has been edited. In the case of JPEG, even a composite with parts subjected to matching compressions will have a difference in the compression artifacts. In order to make the typically faint compression artifacts more readily visible, the data to be analyzed is subjected to an additional round of lossy compression, this time at a known, uniform level, and the result is subtracted from the original data under investigation. The resulting difference image is then inspected manually for any variation in the level of compression


id: facet-theory-20260505-0
file: data/en.wikipedia.org/wiki/Facet_theory-0.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Facet theory is a metatheory for the multivariate behavioral sciences that posits that scientific theories and measurements can be advanced by discovering relationships between conceptual classifications of research variables and empirical partitions of data-representation spaces. For this purpose, facet theory proposes procedures for (1) Constructing or selecting variables for observation, using the mapping sentence technique (a formal definitional framework for a system of observations), and (2) Analyzing multivariate data, using data representation spaces, notably those depicting similarity measures (e.g., correlations), or partially ordered sets, derived from the data. Facet theory is characterized by its direct concern with the entire content-universe under study, containing many, possibly infinitely many, variables. Observed variables are regarded just as a sample of statistical units from the multitude of variables that make up the investigated attribute (the content-universe). Hence, Facet theory proposes techniques for sampling variables for observation from the entire content universe; and for making inferences from the sample of observed variables to the entire content universe. The sampling of variables is done with the aid of the mapping sentence technique (see Section 1); and inferences from the sample of observed variables to the entire content universe are made with respect to correspondences between conceptual


id: facet-theory-20260505-1
file: data/en.wikipedia.org/wiki/Facet_theory-1.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
An initial framework for observing intelligence could be Mapping Sentence 1. The mapping sentence serves as a unified semantic device for specifying the system of intelligence test items, according to the present conceptualization. Its content facet, the material facet, may now serve as a classification of intelligence test items to be considered. Thus, in designing observations, a stratified sampling of items is afforded by ensuring an appropriate selection of items from each of the material facet elements; that is, from each class of items: the verbal, the numerical and the spatial. === Enriching the mapping sentence === The research design can be enriched by introducing to the mapping sentence an additional, independent classification of the observations in the form of an additional content-facet, thereby facilitating systematic differentiations of the observations. For example, intelligence items may be classified also according to the cognitive operation required in order to respond correctly to an item: whether rule-recall (memory), rule-application, or rule-inference. Instead of the three sub-content-universes of intelligence defined by the material facet alone, we now have nine sub-content-universes defined by the cartesian multiplication of the material and the mental-operation facets. See mapping sentence 2. Another way of enriching a mapping sentence (and


id: facet-theory-20260505-2
file: data/en.wikipedia.org/wiki/Facet_theory-2.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Receiving as input (or computing from input data) a matrix of similarity coefficients, specifying, for each pair of items how similar they are. A common example is the computation of a correlation-coefficient matrix from input data, where the size of a correlation coefficient between two variables reflects the degree of similarity between them. Mapping the items (variables) as points in a geometric space of a given dimensionality while preserving as well as possible the condition: If rij>rkl then dij<dkl for all i,j,k,l where rij is the similarity measure (e.g., correlation coefficient) between variables i,j and dij is the distance between their points in the space. Most often, Euclidean distance function (Minkowsky distance of order 2) is used. But other distance functions, especially the Manhattan distance function (Minkowsky distance of order 1) are called for. (See Subsection Relating POSAC Measurement Space to the SSA Concept Space below.) The goodness-of-fit of the resulting mapping may be assessed by a loss function– Kruskal's Stress coefficient or Guttman's Coefficient of Alienation. Partitioning the space as well as possible, into simple regions (stripe, sectors or concentric rings) whose variables are in 1-1 correspondence with a pre-conceived content-facet. To run this option, content facet(s) must be


id: facet-theory-20260505-3
file: data/en.wikipedia.org/wiki/Facet_theory-3.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
=== Principles of faceted SSA: A summary === 1. The attribute under study is represented by a geometric space. 2. Variables of the attribute are represented as points in that space. Conversely, every point in the geometric space is a variable of the attribute. This is the Continuity Principle. 3. The observed variables, located as points in the empirical Faceted SSA map, constitute but a sample drawn from the many (possibly infinitely many) variables constituting the content universe of the attribute investigated. 4. The observed variables chosen for SSA must all belong to the same content universe. This is ensured by including in the SSA only variables whose ranges are similarly ordered with respect to a common meaning (CMR). 5. The sample of variables marked on the Faceted SSA map is used as a guide for inferring possible partitions of the SSA-attribute-map into distinct regions, each region representing a component, or subdomain, of the attribute. 6. In Facet Theory, relationships between attribute components (such as verbal intelligence and numeric intelligence as components of intelligence), are expressed in geometric terms –such as shapes and spatial orientation – rather than in algebraic terms. Just as one would describe relationships between neighboring countries


id: facet-theory-20260505-4
file: data/en.wikipedia.org/wiki/Facet_theory-4.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Variables processed by Facet Theory measurement operations to be described below, evenly cover the attribute content universe. To ensure such coverage, Facet Theory measurement operations are often performed not on the sample of the observed items themselves, but rather on composite variables that represent facet elements that had been validated by Faceted SSA. The sample of individuals is rich enough to allow existing score-profiles of the processed variables to be observed. In the resulting measurement, order relations among individuals should preserve sufficiently well order relations (including comparability and incomparability; see below) between individuals' profiles of the processed variables. The result of the measurement operation yields the smallest number of scales; The resultant scales represent fundamental variables whose interpretation derives from the contents of the observed items, but does not depend on the particular sample of items observed. Partial order analysis of observed data. Let observed items v1,...,vn with a common-meaning range (CMR) represent an investigated content universe; let A1,...,An be their ranges with each Aj ordered from high to low with respect to the common meaning; and let A = A1×A2 × ... × An be the cartesian product of all the range facets, Aj (j = 1,...,n). A system


id: facet-theory-20260505-5
file: data/en.wikipedia.org/wiki/Facet_theory-5.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Given this POSAC solution, an attempt is made to interpret the two coordinates, X1 and X2, as two fundamental scales of the investigated phenomenon of evening TV watching by the investigated population. This is done by, first, interpreting the intervals (equivalence classes) within each coordinate, and then trying to conceptualize the derived meanings of the ordered intervals, in terms of a meaningful notion that may be attributed to the coordinate. In the present simplified example, this is easy: Inspecting the map, we attempt to identify the feature that distinguishes all profiles with given score in X1. Thus, we find that profiles with X1=4, and only they, represent TV watching in the fourth hour. Profiles with X1 = 3 all have 1 in the third watching hour but 0 at the fourth hour, i.e., the third hour is the latest watching hour. X1 = 2 is assigned to, and only to, profiles whose latest watching hour is the second hour. And, finally, X1 = 1 is for the profile 1000 which represents the fact that the first hour is the only – and therefore the latest – watching hour (ignoring the profile 0000 of those who didn't watch TV at the


id: facet-theory-20260505-6
file: data/en.wikipedia.org/wiki/Facet_theory-6.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Interval 1. Low Utility & Low Equality DJ Attitude Interval 2. Low Utility & High Equality DJ Attitude Interval 3. High Utility s & Low Corrective Action DJ Attitude Interval 4. High Utility & High Corrective Action DJ Attitude That is, Enhanced Utility Attitude, even if low, (interval 1 and 2) is somewhat present when Equality is favored (interval 2). If Enhanced Utility Attitude is high (intervals 3 and 4), it reaches the extreme level (interval 4) when Corrective Action is favored. (This may well reflect the sentiment that, in the long run, the advancement of disadvantaged pupils serves the common good.) The meanings of the fundamental variables, X and Y, while relying on the concepts of fairness and of utility, respectively, suggest new notions that modify them. The new notions were christened Enhanced (or Extended) Fairness and Enhanced (or Extended) Utility. === Complementary topics in partial order spaces === Higher order partition lines. The above simple measurement space illustrates partition-lines that are straight or have one bend. More complex measurement spaces result with items whose partition-lines have two or more bends. While partial order spaces are used mainly for analyzing score profiles (based on range facets), under certain conditions,


id: facet-theory-20260505-7
file: data/en.wikipedia.org/wiki/Facet_theory-7.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
== Facet theory: comparisons and comments == Concerned with the entire cycle of multivariate research – concept definition, observational design, and data analysis for concept-structure and measurement, Facet Theory constitutes a novel paradigm for the behavioral sciences. Hence, only limited aspects of it can be compared with specific statistical methods. A distinctive feature of Facet Theory is its explicit concern with the entire set of variables included in the investigated content-universe, regarding the subset of observed variables as but a sample from which inferences can be made. Hence, clusters of variables, if observed, are of no significance. They are simply unimportant artifacts of the procedure for sampling of the variables. This is in contrast with cluster analysis or factor analysis where recorded clustering patterns determine research results and interpretations. There have been various attempts to describe technical differences between Factor Analysis and Facet Theory. Briefly, it may be said that while Factor Analysis aims to structure the set of variables selected for observation, Facet Theory aims to structure the entire content universe of all variables, observed as well as unobserved, relying on the continuity principle and using regional hypotheses as an inferential procedure. Guttman's SSA, as well as Multidimensional Scaling


id: feature-engineering-20260505-0
file: data/en.wikipedia.org/wiki/Feature_engineering-0.md
category: reference
title: Feature engineering
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Feature_engineering
date_saved: 2026-05-05T09:54:11.527727+00:00
instance: kb-cron
parent: feature-engineering-20260505-0
summary:
Feature engineering is a preprocessing step in supervised machine learning and statistical modeling which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant information, feature engineering significantly enhances their predictive accuracy and decision-making capability. Beyond machine learning, the principles of feature engineering are applied in various scientific fields, including physics. For example, physicists construct dimensionless numbers such as the Reynolds number in fluid dynamics, the Nusselt number in heat transfer, and the Archimedes number in sedimentation. They also develop first approximations of solutions, such as analytical solutions for the strength of materials in mechanics. == Clustering == One of the applications of feature engineering has been clustering of feature-objects or sample-objects in a dataset. Especially, feature engineering based on matrix decomposition has been extensively used for data clustering under non-negativity constraints on the feature coefficients. These include Non-Negative Matrix Factorization (NMF), Non-Negative Matrix-Tri Factorization (NMTF), Non-Negative Tensor Decomposition/Factorization (NTF/NTD), etc. The non-negativity constraints on coefficients of the feature vectors mined by the above-stated algorithms yields a part-based representation, and different factor matrices exhibit natural clustering properties. Several extensions of the above-stated feature engineering methods have been


id: geographic-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Geographic_analytics-0.md
category: reference
title: Geographic analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Geographic_analytics
date_saved: 2026-05-05T09:54:12.686727+00:00
instance: kb-cron
parent: geographic-analytics-20260505-0
summary:
Geographic analytics is an analytical approach to strategic management and data analytics to make geographic decisions efficiently. Examples of such decisions are choosing the location for a warehouse or planning the regions for a marketing campaign. Data, information and framing conditions are visualized on maps to derive recommendations for action. In comparison to geographic information systems (GIS), which primarily aim at the representation of information on maps (descriptive analytics), Geographic analytics additionally focuses on making business decisions based on the data visualization on the map (prescriptive analytics). == Background == Purely mathematically based approaches that are used in data analytics in order to support management decisions often have the disadvantage that they require a large amount of data. This often results in considerable efforts for gathering, cleaning and understanding the data. Furthermore, there can be “intangible” framing conditions that are disruptive to any purely data-driven optimization solution. Example logistics: For the task to find the optimal location for a warehouse in a logistics network, so-called Center of Gravity models are being used. To minimize the cost, these models use transport volume data, customer locations, cost data, etc., in order to determine the optimal location. However, there are often framing conditions


id: health-care-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Health_care_analytics-0.md
category: reference
title: Health care analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Health_care_analytics
date_saved: 2026-05-05T09:54:13.909352+00:00
instance: kb-cron
parent: health-care-analytics-20260505-0
summary:
Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare: (1) claims and cost data, (2) pharmaceutical and research and development (R&D) data, (3) clinical data (such as collected from electronic medical records (EHRs)), and (4) patient behaviors and preferences data (e.g. patient satisfaction or retail purchases, such as data captured in stores selling personal health products). Health care analytics is a growing industry in many countries including the United States, where it is expected to grow to more than $31 billion by 2022. It is also increasingly important to governments and public health agencies to support health policy and meet public expectations for transparency, as accelerated by the COVID-19 pandemic. Health care analytics allows for the examination of patterns in various healthcare data in order to determine how clinical care can be improved for patients and provider teams, while limiting excessive spending and improving the health of populations. Areas of the industry focuses on clinical analysis, financial analysis, supply chain analysis, as well as marketing, fraud and HR analysis. There is increasing demand in many countries to incorporate social indicators of patients and providers within


id: health-care-analytics-20260505-1
file: data/en.wikipedia.org/wiki/Health_care_analytics-1.md
category: reference
title: Health care analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Health_care_analytics
date_saved: 2026-05-05T09:54:13.909352+00:00
instance: kb-cron
parent: health-care-analytics-20260505-0
summary:
=== Federal government role in health IT === In the United States, multiple federal entities are heavily involved in health analytics infrastructure. Within the executive branch, the administration itself, Centers for Medicare and Medicaid Services (CMS), and Office of the National Coordinator for Health Information Technology (ONC) each have strategic plans and are involved in determining regulation. Within the legislative branch, multiple committees within the House of Representatives and Senate hold hearings and have opinions on using data and technology to reduce costs and improve outcomes in healthcare. The ONC issued the Federal Health IT Strategic Plan 2015-2020. The plan outlines the steps federal agencies will take to achieve widespread use of health information technology (health IT) and electronic health information to enhance the health IT infrastructure, to advance person-centered and self-managed health, to transform health care delivery and community health, and to foster research, scientific knowledge and innovation. The plan is intended “to provide clarity in federal policies, programs, and actions and includes strategies to align program requirements, harmonize and simplify regulations, and aims to help health IT users to advance the learning health system to achieve better health.” The Strategic Plan includes several key initiatives employing multiple strategies


id: iconography-of-correlations-20260505-0
file: data/en.wikipedia.org/wiki/Iconography_of_correlations-0.md
category: reference
title: Iconography of correlations
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Iconography_of_correlations
date_saved: 2026-05-05T09:54:15.084817+00:00
instance: kb-cron
parent: iconography-of-correlations-20260505-0
summary:
In exploratory data analysis, the iconography of correlations, or representation of correlations, is a data visualization technique which replaces a numeric correlation matrix by its graphical projection onto a diagram. Correlations between variables are represented by solid lines (positive correlations) or dotted lines (negative correlations) plotted on the diagram, where shorter lengths or thicker lines (or both) represent greater projection components and thus greater degree of correlation. == History == The idea of using graphical models to represent correlations has a notable application in genomic mapping, though the iconography of correlations is more general for not assuming the probability distribution, since it only relies on representing the correlation coefficients geometrically. The iconography of correlations first dates to 1975, applied to marine geochemistry in a 1981 thesis, and later in a 1982 data analysis article. Afterwards, the method was applied widely in the aerospace industry but for about fifteen years manufacturers kept it fairly confidential; generally, they preferred not to broadcast useful techniques to their competitors. In 1997 the first company was incorporated to distribute iconography of correlations software. Since then the topic of iconography of correlations has been incorporated into university courses, and typical topical articles' citation lists have rapidly


id: interleaving-distance-20260505-0
file: data/en.wikipedia.org/wiki/Interleaving_distance-0.md
category: reference
title: Interleaving distance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interleaving_distance
date_saved: 2026-05-05T09:54:16.289352+00:00
instance: kb-cron
parent: interleaving-distance-20260505-0
summary:
In topological data analysis, the interleaving distance is a measure of similarity between persistence modules, a common object of study in topological data analysis and persistent homology. The interleaving distance was first introduced by Frédéric Chazal et al. in 2009. since then, it and its generalizations have been a central consideration in the study of applied algebraic topology and topological data analysis. == Definition == A persistence module V {\displaystyle \mathbb {V} } is a collection ( V t ∣ t ∈ R ) {\displaystyle (V_{t}\mid t\in \mathbb {R} )} of vector spaces indexed over the real line, along with a collection ( v t s : V s → V t ∣ s ≤ t ) {\displaystyle (v_{t}^{s}:V_{s}\to V_{t}\mid s\leq t)} of linear maps such that v t t {\displaystyle v_{t}^{t}} is always an isomorphism, and the relation v t s ∘ v s r = v t r {\displaystyle v_{t}^{s}\circ v_{s}^{r}=v_{t}^{r}} is satisfied for every r ≤ s ≤ t {\displaystyle r\leq s\leq t} . The case of R {\displaystyle \mathbb {R} } indexing is presented here for simplicity, though the interleaving distance can be readily adapted to more general settings, including multi-dimensional persistence modules. Let U {\displaystyle


id: item-tree-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Item_tree_analysis-0.md
category: reference
title: Item tree analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Item_tree_analysis
date_saved: 2026-05-05T09:54:17.467755+00:00
instance: kb-cron
parent: item-tree-analysis-20260505-0
summary:
Item tree analysis (ITA) is a data analytical method which allows constructing a hierarchical structure on the items of a questionnaire or test from observed response patterns. Assume that we have a questionnaire with m items and that subjects can answer positive (1) or negative (0) to each of these items, i.e. the items are dichotomous. If n subjects answer the items this results in a binary data matrix D with m columns and n rows. Typical examples of this data format are test items which can be solved (1) or failed (0) by subjects. Other typical examples are questionnaires where the items are statements to which subjects can agree (1) or disagree (0). Depending on the content of the items it is possible that the response of a subject to an item j determines her or his responses to other items. It is, for example, possible that each subject who agrees to item j will also agree to item i. In this case we say that item j implies item i (short i → j {\displaystyle i\rightarrow j} ). The goal of an ITA is to uncover such deterministic implications from the data set D. == Algorithms for ITA


id: item-tree-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Item_tree_analysis-1.md
category: reference
title: Item tree analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Item_tree_analysis
date_saved: 2026-05-05T09:54:17.467755+00:00
instance: kb-cron
parent: item-tree-analysis-20260505-0
summary:
== Notes == == References == Bart, W. M., & Krus, D. J. (1973). An ordering-theoretic method to determine hierarchies among items. Educational and psychological measurement, 33, 291–300. Duquenne V (1987). Conceptual Implications Between Attributes and some Representation Properties for Finite Lattices. In B Ganter, R Wille, K Wolfe (eds.), Beiträge zur Begriffsanalyse: Vorträge der Arbeitstagung Begriffsanalyse, Darmstadt 1986, pp. 313–339. Wissenschafts-Verlag, Mannheim. Flament C (1976). L’Analyse Bool´eenne de Questionnaire. Mouton, Paris. Held, T., & Korossy, K. (1998). Data-analysis as heuristic for establishing theoretically founded item structures. Zeitschrift für Psychologie, 206, 169–188. Janssens, R. (1999). A Boolean approach to the measurement of group processes and attitudes. The concept of integration as an example. Mathematical Social Sciences, 38, 275–293. Schrepp M (1999). On the Empirical Construction of Implications on Bi-valued Test Items. Mathematical Social Sciences, 38(3), 361–375. Schrepp, M (2002). Explorative analysis of empirical data by boolean analysis of questionnaires. Zeitschrift für Psychologie, 210/2, S. 99-109. Schrepp, M. (2003). A method for the analysis of hierarchical dependencies between items of a questionnaire. Methods of Psychological Research, 19, 43–79. Schrepp, M. (2006). ITA 2.0: A program for Classical and Inductive Item Tree Analysis. Journal of Statistical Software, Vol. 16, Issue 10. Schrepp,


id: key-value-database-20260505-0
file: data/en.wikipedia.org/wiki/Key–value_database-0.md
category: reference
title: Key–value database
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Key–value_database
date_saved: 2026-05-05T09:54:18.590764+00:00
instance: kb-cron
parent: key-value-database-20260505-0
summary:
A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them. These records are stored and retrieved using a key that uniquely identifies the record, and is used to find the data within the database. Key-value databases differ from the better known relational databases (RDB). RDBs pre-define the data structure in the database as a series of tables containing fields with well-defined data types. Exposing the data types to the database program allows it to apply various optimizations. In contrast, key-value systems treat the value as opaque to the database itself, and typically support only simple operations such as storing, retrieving, updating, and deleting a value by its key. This offers considerable flexibility and makes such systems well suited to low-latency, high-throughput workloads dominated by direct key lookups, but less suitable for applications that require complex queries or explicit relationships among records. A lack of standardization, limited transaction support, and relatively simple query interfaces long restricted many key-value systems to specialized uses, but the rapid


id: l1-norm-principal-component-analysis-20260505-0
file: data/en.wikipedia.org/wiki/L1-norm_principal_component_analysis-0.md
category: reference
title: L1-norm principal component analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/L1-norm_principal_component_analysis
date_saved: 2026-05-05T09:54:19.890827+00:00
instance: kb-cron
parent: l1-norm-principal-component-analysis-20260505-0
summary:
L1-norm principal component analysis (L1-PCA) is a general method for multivariate data analysis. L1-PCA is often preferred over standard L2-norm principal component analysis (PCA) when the analyzed data may contain outliers (faulty values or corruptions), as it is believed to be robust. Both L1-PCA and standard PCA seek a collection of orthogonal directions (principal components) that define a subspace wherein data representation is maximized according to the selected criterion. Standard PCA quantifies data representation as the aggregate of the L2-norm of the data point projections into the subspace, or equivalently the aggregate Euclidean distance of the original points from their subspace-projected representations. L1-PCA uses instead the aggregate of the L1-norm of the data point projections into the subspace. In PCA and L1-PCA, the number of principal components (PCs) is lower than the rank of the analyzed matrix, which coincides with the dimensionality of the space defined by the original data points. Therefore, PCA or L1-PCA are commonly employed for dimensionality reduction for the purpose of data denoising or compression. Among the advantages of standard PCA that contributed to its high popularity are low-cost computational implementation by means of singular-value decomposition (SVD) and statistical optimality when the data set is generated


id: l1-norm-principal-component-analysis-20260505-1
file: data/en.wikipedia.org/wiki/L1-norm_principal_component_analysis-1.md
category: reference
title: L1-norm principal component analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/L1-norm_principal_component_analysis
date_saved: 2026-05-05T09:54:19.890827+00:00
instance: kb-cron
parent: l1-norm-principal-component-analysis-20260505-0
summary:
The transition from (5) to (8) for K = 1 {\displaystyle K=1} holds true, since the unique singular value of X b {\displaystyle \mathbf {X} \mathbf {b} } is equal to ‖ X b ‖ 2 = b ⊤ X ⊤ X b {\displaystyle \|\mathbf {X} \mathbf {b} \|_{2}={\sqrt {\mathbf {b} ^{\top }\mathbf {X} ^{\top }\mathbf {X} \mathbf {b} }}} , for every b {\displaystyle \mathbf {b} } . Then, if b BNM {\displaystyle \mathbf {b} _{\text{BNM}}} is the solution to BQM in (7), it holds that is the exact L1-PC of X {\displaystyle \mathbf {X} } , as defined in (1). In addition, it holds that b BNM = sgn ( X ⊤ q L1 ) {\displaystyle \mathbf {b} _{\text{BNM}}={\text{sgn}}(\mathbf {X} ^{\top }\mathbf {q} _{\text{L1}})} and ‖ X ⊤ q L1 ‖ 1 = ‖ X b BNM ‖ 2 {\displaystyle \|\mathbf {X} ^{\top }\mathbf {q} _{\text{L1}}\|_{1}=\|\mathbf {X} \mathbf {b} _{\text{BNM}}\|_{2}} . == Algorithms == === Exact solution of exponential complexity === As shown above, the exact solution to L1-PCA can be obtained by the following two-step process: 1. Solve the problem in (5) to obtain B BNM {\displaystyle \mathbf {B} _{\text{BNM}}} . 2. Apply SVD on X B


id: lesion-network-mapping-20260505-0
file: data/en.wikipedia.org/wiki/Lesion_network_mapping-0.md
category: reference
title: Lesion network mapping
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Lesion_network_mapping
date_saved: 2026-05-05T09:54:21.110794+00:00
instance: kb-cron
parent: lesion-network-mapping-20260505-0
summary:
Lesion network mapping is a neuroimaging technique that analyzes the connectivity pattern of brain lesions to identify neuroanatomic correlates of symptoms. The scientific validity of these methods has been widely disputed. The technique was developed by Aaron Boes to understand the network anatomy of lesion induced neurologic and psychiatric symptoms that can not be explained by focal anatomic localization. Lesion network mapping applies a network-based approach to identify connected brain networks, rather than focal brain regions, that correlate with a specific symptom. In focal neuroanatomic localization, developed by Paul Broca and others, specific symptoms that occur due to brain lesions can be understood by identifying a specific brain region that is injured by lesions to establish brain-symptom relationships. However, a number of neurologic symptoms, such as peduncular hallucinosis, are not amenable to this approach since the lesions associated with the symptom do not map to one focal brain location. Lesion network mapping helps to explain these lesion-induced syndromes by showing that lesion locations associated with a given symptom all map to a shared brain network even if they do not all map to a focal brain region. The technique maps the location of lesions associated with a specific symptom and


id: mathematical-biosciences-institute-20260505-0
file: data/en.wikipedia.org/wiki/Mathematical_Biosciences_Institute-0.md
category: reference
title: Mathematical Biosciences Institute
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mathematical_Biosciences_Institute
date_saved: 2026-05-05T09:54:22.360776+00:00
instance: kb-cron
parent: mathematical-biosciences-institute-20260505-0
summary:
The Mathematical Biosciences Institute (MBI) was an institution of higher learning affiliated with the Ohio State University in Columbus, Ohio. MBI received major funding from the National Science Foundation. == History == Under the leadership of founding director Avner Friedman, MBI opened in September 2002, holding its first workshop, hosting its first visiting researchers, and starting its first cohort of postdocs in that month. MBI holds 10–12 scientific workshops each year, and hosts about 25 postdoctoral and visiting researchers in residence at any given time. Through its collective events and programs, MBI draws over 1000 visits by researchers in the broadly defined area of mathematical biology throughout the year. MBI's long term planning is overseen by its Directorate and its Board of Trustees, while its scientific activities are overseen by its Directorate and its Scientific Advisory Committee. == MBI programs == === Workshops === MBI organizes Emphasis Semesters consisting of three or four week-long workshops. Emphasis Semesters are organized around selected themes which have included Mathematical Neuroscience, Cancer and Its Environment, and Analysis of Complex Data in Biological Systems. Outside of Emphasis Semesters, Current Topic Workshops focus on emerging topics in the mathematical biosciences. Most of the institute's programs are


id: meter-data-management-20260505-0
file: data/en.wikipedia.org/wiki/Meter_data_management-0.md
category: reference
title: Meter data management
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Meter_data_management
date_saved: 2026-05-05T09:54:23.521041+00:00
instance: kb-cron
parent: meter-data-management-20260505-0
summary:
Meter data management (MDM) refers to software that performs long-term data storage and management for the vast quantities of data delivered by smart metering systems. This data consists primarily of usage data and events that are imported from the head-end servers managing the data collection in advanced metering infrastructure (AMI) or automatic meter reading (AMR) systems. MDM is a component in the smart grid infrastructure promoted by utility companies. This may also incorporate meter data analytics, the analysis of data emitted by electric smart meters that record consumption of electric energy. == MDM systems == An MDM system will typically import the data, then validate, cleanse and process it before making it available for billing and analysis. Products for meter data include: Smart meter deployment planning and management; Meter and network asset monitoring and management; Automated smart meter provisioning (i.e. addition, deletion and updating of meter information at utility and AMR side) and billing cutover; Meter-to-cash system, workforce management system, asset management and other systems. Furthermore, an MDM may provide reporting capabilities for load and demand forecasting, management reports, and customer service metrics. An MDM provide application programming interfaces (APIs) between the MDM and the multiple destinations that rely on


id: multi-model-database-20260505-0
file: data/en.wikipedia.org/wiki/Multi-model_database-0.md
category: reference
title: Multi-model database
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multi-model_database
date_saved: 2026-05-05T09:54:24.674447+00:00
instance: kb-cron
parent: multi-model-database-20260505-0
summary:
In the field of database design, a multi-model database is a database management system designed to support multiple data models against a single, integrated backend. In contrast, most database management systems are organized around a single data model that determines how data can be organized, stored, and manipulated. Document, graph, relational, and key–value models are examples of data models that may be supported by a multi-model database. == Background == The relational data model became popular after its publication by Edgar F. Codd in 1970. Due to increasing requirements for horizontal scalability and fault tolerance, NoSQL databases became prominent after 2009. NoSQL databases use a variety of data models, with document, graph, and key–value models being popular. A multi-model database is a database that can store, index and query data in more than one model. For some time, databases have primarily supported only one model, such as: relational database, document-oriented database, graph database or triplestore. A database that combines many of these is multi-model. This should not be confused with multimodal database systems such as Pixeltable or ApertureDB, which focus on unified management of different media types (images, video, audio, text) rather than different data models. For some time, it


id: multivariate-logistic-regression-20260505-0
file: data/en.wikipedia.org/wiki/Multivariate_logistic_regression-0.md
category: reference
title: Multivariate logistic regression
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multivariate_logistic_regression
date_saved: 2026-05-05T09:54:25.883559+00:00
instance: kb-cron
parent: multivariate-logistic-regression-20260505-0
summary:
Multivariate logistic regression is a type of data analysis that predicts any number of outcomes based on multiple independent variables. It is based on the assumption that the natural logarithm of the odds has a linear relationship with independent variables. == Procedure == First, the baseline odds of a specific outcome compared to not having that outcome are calculated, giving a constant (intercept). Next, the independent variables are incorporated into the model, giving a regression coefficient (beta) and a "P" value for each independent variable. The "P" value determines how significantly the independent variable impacts the odds of having the outcome or not. It is desirable to use as few variables as necessary, and to have at least 10 - 20 times as many observations as independent variables. === Formula === Multivariate logistic regression uses a formula similar to univariate logistic regression, but with multiple independent variables. where v is the number of independent variables. The following formula shows that multivariate logistic regression is simply a standard linear regression model: == Types == The two main types of multivariate logistic regression are linear regression and logistic regression. === Linear regression === Linear regression produces results that show a linear relationship


id: multiway-data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Multiway_data_analysis-0.md
category: reference
title: Multiway data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multiway_data_analysis
date_saved: 2026-05-05T09:54:27.058609+00:00
instance: kb-cron
parent: multiway-data-analysis-20260505-0
summary:
Multiway data analysis is a method of analyzing large data sets by representing a collection of observations as a multiway array, A ∈ C I 0 × I 1 × … I c × … I C {\displaystyle {\mathcal {A}}\in {\mathbb {C} }^{I_{0}\times I_{1}\times \dots I_{c}\times \dots I_{C}}} . The proper choice of data organization into (C+1)-way array, and analysis techniques can reveal patterns in the underlying data undetected by other methods. == History == The study of multiway data analysis was first formalized as the result of a conference held in 1988. The result of this conference was the first text specifically addressed to this field, Coppi and Bolasco's Multiway Data Analysis. At that time, the application areas for multiway analysis included statistics, econometrics and psychometrics. In recent years, applications have expanded to include chemometrics, agriculture, social network analysis and the food industry. == Composition of multiway data analysis == === Multiway data === Multiway data analysts use the term way to refer to the number sources of data variation while reserving the word mode for the methods or models used to analyze the data. In this sense, we can define the various ways of data to analyze: One


id: narrative-intelligence-20260505-0
file: data/en.wikipedia.org/wiki/Narrative_intelligence-0.md
category: reference
title: Narrative intelligence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Narrative_intelligence
date_saved: 2026-05-05T09:54:28.284976+00:00
instance: kb-cron
parent: narrative-intelligence-20260505-0
summary:
Narrative Intelligence refers to the capability of identifying, understanding and responding to narrative attacks that cause financial reputational, operational or physical harm. It is studied at the intersection of artificial intelligence (AI), cybersecurity, linguistics, psychology, and cognitive science. The term is used to describe how computational systems and humans structure, interpret, and apply stories in communication, reasoning, and decision-making. == Definition and scope == Narrative intelligence encompasses three primary areas: story identification, story understanding, and narrative response. Story identification refers to the capacity to identify harmful narratives by their structural components, including characters, events, settings, goals, and causal relationships. In computational research, this involves extracting structured representations from natural language texts in order to model the underlying narrative structure. Story understanding concerns knowing the context of coherent and contextually consistent narratives by computational systems. Approaches to story understanding include rule-based systems, planning-based models, and AI-based learning techniques. These systems aim to produce narratives that maintain logical consistency across events and adhere to temporal and causal constraints. Narrative response involves drawing inferences from narrative content and then making decisions on how to respond to them. This includes modeling causal and temporal relationships, predicting possible outcomes, and supporting decision-making processes based on


id: necessary-condition-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Necessary_condition_analysis-0.md
category: reference
title: Necessary condition analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Necessary_condition_analysis
date_saved: 2026-05-05T09:54:29.420634+00:00
instance: kb-cron
parent: necessary-condition-analysis-20260505-0
summary:
Necessary condition analysis (NCA) is a research approach and tool employed to discern "necessary conditions" within datasets. These indispensable conditions stand as pivotal determinants of particular outcomes, wherein the absence of such conditions ensures the absence of the intended result. For example, the admission of a student into a Ph.D. program necessitates a prior degree; the progression of AIDS necessitates the presence of HIV; and organizational change necessitates communication. The absence these conditions guarantees the outcome cannot occur, and no other condition can overcome the lack of this condition. Further, necessary conditions are not always sufficient. For example, AIDS necessitates HIV, but HIV does not always cause AIDS. In such instances, the condition demonstrates its necessity but lacks sufficiency. NCA seeks to use statistical methods to test for such conditions. == Overview == Traditional statistical methods often emphasize the identification of factors that are sufficient to produce an outcome. In contrast, NCA aims to uncover conditions that must be present for a specific outcome to occur. While researchers will sometimes use NCA as a stand-alone analysis, they often use it to add additional depth to existing analyses of data. For example, NCA acts as stand-alone method or as a complement


id: negative-testing-20260505-0
file: data/en.wikipedia.org/wiki/Negative_testing-0.md
category: reference
title: Negative testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Negative_testing
date_saved: 2026-05-05T09:54:30.657459+00:00
instance: kb-cron
parent: negative-testing-20260505-0
summary:
Negative testing is a method of testing an application or system to improve the likelihood that an application works as intended/specified and can handle unexpected input and user behavior. Invalid data is inserted to compare the output against the given input. Negative testing is also known as failure testing or error path testing. When performing negative testing exceptions are expected. This shows that the application is able to handle improper user behavior. Users input values that do not work in the system to test its ability to handle incorrect values or system failure. == Purpose == The purpose of negative testing is to prevent the application from crashing and it also helps improve the quality of an application by detecting defects. Negative testing helps you to improve the testing coverage of the application. Negative testing makes the application more stable and reliable. Negative testing together with positive testing allows users to test the application with any valid (or invalid) input data. == Benefits of negative testing == Negative testing is done to check that the product deals properly with the circumstance for which it is not programmed. The fundamental aim of this testing is to check how bad data is


id: netvibes-20260505-0
file: data/en.wikipedia.org/wiki/Netvibes-0.md
category: reference
title: Netvibes
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Netvibes
date_saved: 2026-05-05T09:54:31.800507+00:00
instance: kb-cron
parent: netvibes-20260505-0
summary:
Netvibes is a French brand of Dassault Systèmes that previously ran a web service offering a dashboard and feed reader. Currently, the company offers business intelligence tools. == History == === 2005–2012 === Founded in 2005 by Tariq Krim, the company provided software for personalized dashboards for real-time monitoring, social analytics, knowledge sharing, and decision support. === 2012–present === On February 9, 2012, Dassault Systèmes announced the acquisition of Netvibes. As of 2024, Netvibes also contains the operations of two other software companies acquired by Dassault Systèmes: Exalead: founded in 2000 by François Bourdoncle, the company provided search platforms and search-based applications for consumer and business users. On June 9, 2010, Dassault Systèmes acquired the company. Proxem: Founded in 2007 by François-Régis Caumartin, the company provided AI-powered semantic processing software and services. On June 23, 2020, Dassault Systèmes acquired Proxem and integrated its technology into the 3DEXPERIENCE® platform to complement its information intelligence applications. Dassault Systèmes announced in April 2025 that Netvibes would retire its standalone web service offering on June 2, 2025. == Activities == Brand monitoring – to track clients, customers and competitors across media sources all in one place, analyze live results with third party reporting tools,


id: nosql-20260505-0
file: data/en.wikipedia.org/wiki/NoSQL-0.md
category: reference
title: NoSQL
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/NoSQL
date_saved: 2026-05-05T09:54:32.988294+00:00
instance: kb-cron
parent: nosql-20260505-0
summary:
NoSQL (originally meaning "not only SQL" or "non-relational") refers to a type of database design that stores and retrieves data differently from the traditional table-based structure of relational databases. Unlike relational databases, which organize data into rows and columns like a spreadsheet, NoSQL databases use a single data structure—such as key–value pairs, wide columns, graphs, or documents—to hold information. Since this non-relational design does not require a fixed schema, it scales easily to manage large, often unstructured datasets. NoSQL systems are sometimes called "Not only SQL" because they can support SQL-like query languages or work alongside SQL databases in polyglot-persistent setups, where multiple database types are combined. Non-relational databases date back to the late 1960s, but the term "NoSQL" emerged in the early 2000s, spurred by the needs of Web 2.0 companies like social media platforms. NoSQL databases are popular in big data and real-time web applications due to their simple design, ability to scale across clusters of machines (called horizontal scaling), and precise control over data availability. These structures can speed up certain tasks and are often considered more adaptable than fixed database tables. However, many NoSQL systems prioritize speed and availability over strict consistency (per the CAP theorem),


id: nosql-20260505-1
file: data/en.wikipedia.org/wiki/NoSQL-1.md
category: reference
title: NoSQL
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/NoSQL
date_saved: 2026-05-05T09:54:32.988294+00:00
instance: kb-cron
parent: nosql-20260505-0
summary:
=== Multiple queries === Instead of retrieving all the data with one query, it is common to do several queries to get the desired data. NoSQL queries are often faster than traditional SQL queries, so the cost of additional queries may be acceptable. If an excessive number of queries would be necessary, one of the other two approaches is more appropriate. === Caching, replication and non-normalized data === Instead of only storing foreign keys, it is common to store actual foreign values along with the model's data. For example, each blog comment might include the username in addition to a user id, thus providing easy access to the username without requiring another lookup. When a username changes, however, this will now need to be changed in many places in the database. Thus this approach works better when reads are much more common than writes. === Nesting data === With document databases like MongoDB it is common to put more data in a smaller number of collections. For example, in a blogging application, one might choose to store comments within the blog post document, so that with a single retrieval one gets all the comments. Thus in this approach a single


id: offset-filtration-20260505-0
file: data/en.wikipedia.org/wiki/Offset_filtration-0.md
category: reference
title: Offset filtration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Offset_filtration
date_saved: 2026-05-05T09:54:34.194182+00:00
instance: kb-cron
parent: offset-filtration-20260505-0
summary:
The offset filtration (also called the "union-of-balls" or "union-of-disks" filtration) is a growing sequence of metric balls used to detect the size and scale of topological features of a data set. The offset filtration commonly arises in persistent homology and the field of topological data analysis. Utilizing a union of balls to approximate the shape of geometric objects was first suggested by Frosini in 1992 in the context of submanifolds of Euclidean space. The construction was independently explored by Robins in 1998, and expanded to considering the collection of offsets indexed over a series of increasing scale parameters (i.e., a growing sequence of balls), in order to observe the stability of topological features with respect to attractors. Homological persistence as introduced in these papers by Frosini and Robins was subsequently formalized by Edelsbrunner et al. in their seminal 2002 paper Topological Persistence and Simplification. Since then, the offset filtration has become a primary example in the study of computational topology and data analysis. == Definition == Let X {\displaystyle X} be a finite set in a metric space ( M , d ) {\displaystyle (M,d)} , and for any x ∈ X {\displaystyle x\in X} let B ( x ,


id: open-coding-20260505-0
file: data/en.wikipedia.org/wiki/Open_coding-0.md
category: reference
title: Open coding
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_coding
date_saved: 2026-05-05T09:54:35.377999+00:00
instance: kb-cron
parent: open-coding-20260505-0
summary:
Based in grounded theory, open coding is the analytic process through which concepts (codes) are attached to observed data and phenomena during qualitative data analysis. It is one of the techniques described by Strauss (1987) and Strauss and Corbin (1990) for working with text. Open coding attempts to codify, name or classifying the observed phenomenon and is achieved by segmenting data into meaningful expressions and describing that data with a single word or short sequence of words. Relevant annotations and concepts are then attached to these expressions. == Details == Applied in varying degrees of detail, open coding can be linked to a line, sentence, paragraph or complete text (e.g., protocol, scenario). Alternatives are selected according to the research question, relevant data, personal style of the analyst and the stage of research. However, coding should always follow its aim to break down and understand a text and develop a scheme of categories over the course of time. The result of open coding should be a list of characteristic codes and categories attached to the text and supported by code notes to explain the content. These notes could take the form of interesting observations and thoughts that are relevant to the


id: open-data-index-20260505-0
file: data/en.wikipedia.org/wiki/Open_data_index-0.md
category: reference
title: Open data index
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_data_index
date_saved: 2026-05-05T09:54:36.535575+00:00
instance: kb-cron
parent: open-data-index-20260505-0
summary:
An open data index is any set of indicators which assesses and evaluates the general openness of a government's open data portal. Open data indices not only show how open a data portal is, but also encourage citizens and government officials alike, to participate in their local open data communities, particularly in advocating for local open data and local open data policies. There are two mainstream methodologies, which are Global Open Data Index and Open Data Barometer. The Global Open Data Index evaluates an open data portal from 11 different aspects based on the Open Definition of open data, while the Open Data Barometer adds two more indices compared to the previous one. == Scoring standard == According to the service offered by Open Knowledge International, they run a measurement called "Global Open Data Index" which is "an annual effort to measure the state of open government data around the world". And they evaluate the openness of an open dataset according to the following questions: 1. Does the data exist? (5 marks) The Open Knowledge Foundation specifically indicates that the data of an open data portal should be directly comes from the official government department or a third party with


id: opendap-20260505-0
file: data/en.wikipedia.org/wiki/OPeNDAP-0.md
category: reference
title: OPeNDAP
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OPeNDAP
date_saved: 2026-05-05T09:54:37.688368+00:00
instance: kb-cron
parent: opendap-20260505-0
summary:
OPeNDAP (Open-source Project for a Network Data Access Protocol) is an endeavor focused on enhancing the retrieval of remote, structured data through a Web-based architecture and a discipline-neutral Data Access Protocol (DAP). == Project == Widely used, especially in Earth science, the protocol is layered on HTTP, and its current specification is DAP4, though the previous DAP2 version remains broadly used. Developed and advanced (openly and collaboratively) by the non-profit OPeNDAP, Inc., DAP is intended to enable remote, selective data-retrieval as an easily invoked Web service. OPeNDAP, Inc. also develops and maintains zero-cost (reference) implementations of the DAP protocol in both server-side and client-side software. "OPeNDAP" often is used in place of "DAP" to denote the protocol but also may refer to an entire DAP-based data-retrieval architecture. Other DAP-centered architectures, such as THREDDS and ERDDAP, the NOAA GEO-IDE UAF ERDDAP exhibit significant interoperability with one another as well as with systems employing OPeNDAP's own (open-source) servers and software. A DAP client can be an ordinary browser or even a spreadsheet, though with limited functionality (see OPeNDAP's Web page on Available Client Software). More typically, DAP clients are: Data-analysis or data-visualization tools (such as MATLAB, IDL, Panoply, GrADS, Integrated Data Viewer,


id: operational-analytical-processing-20260505-0
file: data/en.wikipedia.org/wiki/Operational_analytical_processing-0.md
category: reference
title: Operational analytical processing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Operational_analytical_processing
date_saved: 2026-05-05T09:54:54.131392+00:00
instance: kb-cron
parent: operational-analytical-processing-20260505-0
summary:
Operational analytical processing, more popularly known as operational analytics, is a subset of data analytics that focuses on improving the operational nature of a business or entity. The main characteristic that distinguishes operational analytics from other types of analytics is that it is analytics on the fly, which means that signals emanating from various parts of a business are processed in real-time to feed back into instant decision-making for the business. This is sometimes referred to as "continuous analytics," which is another way to emphasize the continuous digital feedback loop that can exist from one part of a business to its other parts. == Overview == The rapid digital transformation of many businesses means that an increasing number of business signals are being recorded and stored in digital form. Businesses are using these signals to improve their efficiency, improve their performance and provide better experiences to their users and customers. A Forrester Report details how digitization of a business is impacting its customer experiences by leveraging data. Operational analytics allows you to process various types of information from different sources and then decide what to do next: what action to take, whom to talk to, what immediate plans to make.


id: ordered-key-value-store-20260505-0
file: data/en.wikipedia.org/wiki/Ordered_key–value_store-0.md
category: reference
title: Ordered key–value store
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ordered_key–value_store
date_saved: 2026-05-05T09:54:55.294937+00:00
instance: kb-cron
parent: ordered-key-value-store-20260505-0
summary:
An ordered key–value store (OKVS) is a type of data storage paradigm that can support multi-model databases. An OKVS is an ordered mapping of bytes to bytes. An OKVS will keep the key–value pairs sorted by the key lexicographic order. OKVS systems provides different set of features and performance trade-offs. Most of them are shipped as a library without network interfaces, in order to be embedded in another process. Most OKVS support ACID guarantees. Some OKVS are distributed databases. Ordered key–value stores found their way into many modern database systems including NewSQL database systems. == History == The origin of ordered key–value store stems from the work of Ken Thompson on dbm in 1979. Later in 1991, Berkeley DB was released that featured a B-Tree backend that allowed the keys to stay sorted. Berkeley DB was said to be very fast and made its way into various commercial product. It was included in Python standard library until 2.7. In 2009, Tokyo Cabinet was released that was superseded by Kyoto Cabinet that support both transaction and ordered keys. In 2011, LMDB was created to replace Berkeley DB in OpenLDAP. There is also Google's LevelDB that was forked by Facebook in 2012


id: persistence-module-20260505-0
file: data/en.wikipedia.org/wiki/Persistence_module-0.md
category: reference
title: Persistence module
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Persistence_module
date_saved: 2026-05-05T09:54:56.529466+00:00
instance: kb-cron
parent: persistence-module-20260505-0
summary:
A persistence module is a mathematical structure in persistent homology and topological data analysis that formally captures the persistence of topological features of an object across a range of scale parameters. A persistence module often consists of a collection of homology groups (or vector spaces if using field coefficients) corresponding to a filtration of topological spaces, and a collection of linear maps induced by the inclusions of the filtration. The concept of a persistence module was first introduced in 2005 as an application of graded modules over polynomial rings, thus importing well-developed algebraic ideas from classical commutative algebra theory to the setting of persistent homology. Since then, persistence modules have been one of the primary algebraic structures studied in the field of applied topology. == Definition == === Single Parameter Persistence Modules === Let T {\displaystyle T} be a totally ordered set and let K {\displaystyle K} be a field. The set T {\displaystyle T} is sometimes called the indexing set. Then a single-parameter persistence module M {\displaystyle M} is a functor M : T → V e c K {\displaystyle M:T\to \mathbf {Vec} _{K}} from the poset category of T {\displaystyle T} to the category of vector spaces over


id: persistence-module-20260505-1
file: data/en.wikipedia.org/wiki/Persistence_module-1.md
category: reference
title: Persistence module
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Persistence_module
date_saved: 2026-05-05T09:54:56.529466+00:00
instance: kb-cron
parent: persistence-module-20260505-0
summary:
=== Interval Modules === A primary concern in the study of persistence modules is whether modules can be decomposed into "simpler pieces", roughly speaking. In particular, it is algebraically and computationally convenient if a persistence module can be expressed as a direct sum of smaller modules known as interval modules. Let J {\displaystyle J} be a nonempty subset of a poset P {\displaystyle P} . Then J {\displaystyle J} is an interval in P {\displaystyle P} if For every x , z ∈ J {\displaystyle x,z\in J} if x ≤ y ≤ z ∈ P {\displaystyle x\leq y\leq z\in P} then y ∈ J {\displaystyle y\in J} For every x , z ∈ J {\displaystyle x,z\in J} there is a sequence of elements p 1 , p 2 , … , p n ∈ J {\displaystyle p_{1},p_{2},\dots ,p_{n}\in J} such that p 1 = x {\displaystyle p_{1}=x} , p n = z {\displaystyle p_{n}=z} , and p i , p j {\displaystyle p_{i},p_{j}} are comparable for all i , j ∈ { 1 , … , n } {\displaystyle i,j\in \{1,\dots ,n\}} . Now given an interval J ⊆ P {\displaystyle J\subseteq P} we can define a persistence module I


id: persistent-betti-number-20260505-0
file: data/en.wikipedia.org/wiki/Persistent_Betti_number-0.md
category: reference
title: Persistent Betti number
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Persistent_Betti_number
date_saved: 2026-05-05T09:54:57.708149+00:00
instance: kb-cron
parent: persistent-betti-number-20260505-0
summary:
In persistent homology, a persistent Betti number is a multiscale analog of a Betti number that tracks the number of topological features that persist over multiple scale parameters in a filtration. Whereas the classical n t h {\displaystyle n^{th}} Betti number equals the rank of the n t h {\displaystyle n^{th}} homology group, the n t h {\displaystyle n^{th}} persistent Betti number is the rank of the n t h {\displaystyle n^{th}} persistent homology group. The concept of a persistent Betti number was introduced by Herbert Edelsbrunner, David Letscher, and Afra Zomorodian in the 2002 paper Topological Persistence and Simplification, one of the seminal papers in the field of persistent homology and topological data analysis. Applications of the persistent Betti number appear in a variety of fields including data analysis, machine learning, and physics. == Definition == Let K {\displaystyle K} be a simplicial complex, and let f : K → R {\displaystyle f:K\to \mathbb {R} } be a monotonic, i.e., non-decreasing function. Requiring monotonicity guarantees that the sublevel set K ( a ) := f − 1 ( − ∞ , a ] {\displaystyle K(a):=f^{-1}(-\infty ,a]} is a subcomplex of K {\displaystyle K} for all a ∈ R {\displaystyle


id: persistent-homology-group-20260505-0
file: data/en.wikipedia.org/wiki/Persistent_homology_group-0.md
category: reference
title: Persistent homology group
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Persistent_homology_group
date_saved: 2026-05-05T09:54:58.899489+00:00
instance: kb-cron
parent: persistent-homology-group-20260505-0
summary:
In persistent homology, a persistent homology group is a multiscale analog of a homology group that captures information about the evolution of topological features across a filtration of spaces. While the ordinary homology group represents nontrivial homology classes of an individual topological space, the persistent homology group tracks only those classes that remain nontrivial across multiple parameters in the underlying filtration. Analogous to the ordinary Betti number, the ranks of the persistent homology groups are known as the persistent Betti numbers. Persistent homology groups were first introduced by Herbert Edelsbrunner, David Letscher, and Afra Zomorodian in a 2002 paper Topological Persistence and Simplification, one of the foundational papers in the fields of persistent homology and topological data analysis, based largely on the persistence barcodes and the persistence algorithm, that were first described by Serguei Barannikov in the 1994 paper. Since then, the study of persistent homology groups has led to applications in data science, machine learning, materials science, biology, and economics. == Definition == Let K {\displaystyle K} be a simplicial complex, and let f : K → R {\displaystyle f:K\to \mathbb {R} } be a real-valued monotonic function. Then for some values a 0 < a 1 < ⋯


id: photoanalysis-20260505-0
file: data/en.wikipedia.org/wiki/Photoanalysis-0.md
category: reference
title: Photoanalysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Photoanalysis
date_saved: 2026-05-05T09:55:00.074890+00:00
instance: kb-cron
parent: photoanalysis-20260505-0
summary:
Photoanalysis (or photo analysis) refers to the study of pictures to compile various types of data, for example, to measure the size distribution of virtually anything that can be captured by photo. Photoanalysis technology has changed the way mines and mills quantify fragmented material. Images are an effective way to document conditions before, after, and even during blasting activities. The technology is advancing at a high rate, and lenses, storage media memory, light sensitivity and resolution have been improving steadily. Today's digital cameras and camcorders include high-resolution optics, compact size, automatic time and date stamps, good battery life, shutters to freeze motion, and computers to autofocus and eliminate jitter using image stabilization. == Mining == Photoanalysis in mining operations can provide an automated system that forewarns a company of potential problems with materials, leading to economies and reduced damage caused from over-sized materials. It can also help determine the effectiveness of blasts. A company can use this technology to monitor materials moving on a conveyor belt in an underground environment, to measure piles left over from a blast, and even measure the amount of material being carried by dump trucks or vessels to a destination. Photoanalysis is being used on


id: pivot-table-20260505-0
file: data/en.wikipedia.org/wiki/Pivot_table-0.md
category: reference
title: Pivot table
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pivot_table
date_saved: 2026-05-05T09:55:01.231074+00:00
instance: kb-cron
parent: pivot-table-20260505-0
summary:
A pivot table is a table of values which are aggregations of groups of individual values from a more extensive table (such as from a database, spreadsheet, or business intelligence program) within one or more discrete categories. The aggregations or summaries of the groups of the individual terms might include sums, averages, counts, or other statistics. A pivot table is the outcome of the statistical processing of tabularized raw data and can be used for decision-making. Although pivot table is a generic term, Microsoft held a trademark on the term in the United States from 1994 to 2020. == History == In their book Pivot Table Data Crunching, Bill Jelen and Mike Alexander refer to Pito Salas as the "father of pivot tables". While working on a concept for a new program that would eventually become Lotus Improv, Salas noted that spreadsheets have patterns of data. A tool that could help the user recognize these patterns would help to build advanced data models quickly. With Improv, users could define and store sets of categories, then change views by dragging category names with the mouse. This core functionality would provide the model for pivot tables. Lotus Development released Improv in 1991


id: pivot-table-20260505-1
file: data/en.wikipedia.org/wiki/Pivot_table-1.md
category: reference
title: Pivot table
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pivot_table
date_saved: 2026-05-05T09:55:01.231074+00:00
instance: kb-cron
parent: pivot-table-20260505-0
summary:
=== Columns === Column labels are used to apply a filter to one or more columns that have to be shown in the pivot table. For instance if the "Salesperson" field is dragged to this area, then the table constructed will have values from the column "Sales Person", i.e., one will have a number of columns equal to the number of "Salesperson". There will also be one added column of Total. In the example above, this instruction will create five columns in the table — one for each salesperson, and Grand Total. There will be a filter above the data — column labels — from which one can select or deselect a particular salesperson for the pivot table. This table will not have any numerical values as no numerical field is selected but when it is selected, the values will automatically get updated in the column of "Grand total". === Rows === Row labels are used to apply a filter to one or more rows that have to be shown in the pivot table. For instance, if the "Salesperson" field is dragged on this area then the other output table constructed will have values from the column "Salesperson", i.e., one


id: post-hoc-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Post_hoc_analysis-0.md
category: reference
title: Post hoc analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Post_hoc_analysis
date_saved: 2026-05-05T09:55:02.388216+00:00
instance: kb-cron
parent: post-hoc-analysis-20260505-0
summary:
In a scientific study, post hoc analysis (from Latin post hoc, "after this") consists of statistical analyses that were specified after the data were seen. A post hoc analysis is usually used to explore specific, statistically significant differences between the means of three or more independent groups-- differences detected with an analysis of variance (ANOVA). An ANOVA does not identify the group(s); for that, a post hoc analysis is required. Because each post hoc analysis is effectively a statistical test, conducting multiple post hoc comparisons introduces a family-wise error rate problem, which is a type of multiple testing problem. This increases the likelihood of false positives unless corrected. Post hoc tests are follow-up tests performed after a significant ANOVA result to identify where the differences lie (which specific groups differ). To compensate, multiple post hoc testing procedures are sometimes used, but that is often difficult or impossible to do precisely. Post hoc analysis that is conducted and interpreted without adequate consideration of this problem is sometimes called data dredging (p-hacking) by critics because the statistical associations that it finds are often spurious. In other words, findings from data dredging are invalid or not trustworthy. Post hoc analyses are acceptable when


id: qualitative-comparative-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Qualitative_comparative_analysis-0.md
category: reference
title: Qualitative comparative analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Qualitative_comparative_analysis
date_saved: 2026-05-05T09:55:03.633787+00:00
instance: kb-cron
parent: qualitative-comparative-analysis-20260505-0
summary:
In statistics, qualitative comparative analysis (QCA) is a data analysis based on set theory to examine the relationship of conditions to outcome. QCA describes the relationship in terms of necessary conditions and sufficient conditions. The technique was originally developed by Charles Ragin in 1987 to study data sets that are too small for linear regression analysis but large enough for cross-case analysis. == Summary of technique == In the case of categorical variables, QCA begins by listing and counting all types of cases which occur, where each type of case is defined by its unique combination of values of its independent and dependent variables. For instance, if there were four categorical variables of interest, {A,B,C,D}, and A and B were dichotomous (could take on two values), C could take on five values, and D could take on three, then there would be 60 possible types of observations determined by the possible combinations of variables, not all of which would necessarily occur in real life. By counting the number of observations that exist for each of the 60 unique combination of variables, QCA can determine which descriptive inferences or implications are empirically supported by a data set. Thus, the input to


id: qualitative-comparative-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Qualitative_comparative_analysis-1.md
category: reference
title: Qualitative comparative analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Qualitative_comparative_analysis
date_saved: 2026-05-05T09:55:03.633787+00:00
instance: kb-cron
parent: qualitative-comparative-analysis-20260505-0
summary:
== Response to criticisms == QCA can be performed probabilistically or deterministically with observations of categorical variables. For instance, the existence of a descriptive inference or implication is supported deterministically by the absence of any counter-example cases to the inference; i.e. if a researcher claims condition X implies condition Y, then, deterministically, there must not exist any counterexample cases having condition X, but not condition Y. However, if the researcher wants to claim that condition X is a probabilistic 'predictor' of condition Y, in another similar set of cases, then the proportion of counterexample cases to an inference to the proportion of cases having that same combination of conditions can be set at a threshold value of for example 80% or higher. For each prime implicant that QCA outputs via its logical inference reduction process, the "coverage" — percentage out of all observations that exhibit that implication or inference — and the "consistency" — the percentage of observations conforming to that combination of variables having that particular value of the dependent variable or outcome — are calculated and reported, and can be used as indicators of the strength of such an explorative probabilistic inference. In real-life complex societal processes, QCA


id: random-mapping-20260505-0
file: data/en.wikipedia.org/wiki/Random_mapping-0.md
category: reference
title: Random mapping
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Random_mapping
date_saved: 2026-05-05T09:55:04.806192+00:00
instance: kb-cron
parent: random-mapping-20260505-0
summary:
For data analysis, Random mapping (RM) is a fast dimensionality reduction method categorized as feature extraction method. The RM consists in generation of a random matrix that is multiplied by each original vector and result in a reduced vector. When the data vectors are high-dimensional it is computationally infeasible to use data analysis or pattern recognition algorithms which repeatedly compute similarities or distances in the original data space. It is therefore necessary to reduce the dimensionality before, for example, clustering the data. In a text mining context, it is demonstrated that the document classification accuracy obtained after the dimensionality has been reduced using a random mapping method will be almost as good as the original accuracy if the final dimensionality is sufficiently large (about 100 out of 6000). In fact, it can be shown that the inner product (similarity) between the mapped vectors follows closely the inner product of the original vectors. == See also == Random variable Semantic mapping Random projection == References == Kaski, S. Dimensionality reduction by random mapping: fast similarity computation for clustering. Proceedings of The 1998 IEEE International Joint Conference on Neural Networks, 1998. pp. 413–418. doi: 10.1109/IJCNN.1998.682302


id: real-time-data-20260505-0
file: data/en.wikipedia.org/wiki/Real-time_data-0.md
category: reference
title: Real-time data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Real-time_data
date_saved: 2026-05-05T09:55:05.986758+00:00
instance: kb-cron
parent: real-time-data-20260505-0
summary:
Real-time data (RTD) is information that is delivered immediately after collection. There is no delay in the timeliness of the information provided. Real-time data is often used for navigation or tracking. Such data is usually processed using real-time computing although it can also be stored for later or off-line data analysis. Real-time data is not the same as dynamic data. Real-time data can be dynamic (e.g. a variable indicating current location) or static (e.g. a fresh log entry indicating location at a specific time). == In economics == Real-time economic data, and other official statistics, are often based on preliminary estimates, and therefore are frequently adjusted as better estimates become available. These later adjusted data are called "revised data". The terms real-time economic data and real-time economic analysis were coined by Francis X. Diebold and Glenn D. Rudebusch. Macroeconomist Glenn D. Rudebusch defined real-time analysis as 'the use of sequential information sets that were actually available as history unfolded.' Macroeconomist Athanasios Orphanides has argued that economic policy rules may have very different effects when based on error-prone real-time data (as they inevitably are in reality) than they would if policy makers followed the same rules but had more accurate data


id: semblance-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Semblance_analysis-0.md
category: reference
title: Semblance analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Semblance_analysis
date_saved: 2026-05-05T09:55:07.132203+00:00
instance: kb-cron
parent: semblance-analysis-20260505-0
summary:
Semblance analysis is a process used in the refinement and study of seismic data. The use of this technique along with other methods makes it possible to greatly increase the resolution of the data despite the presence of background noise. The new data received following the semblance analysis is usually easier to interpret when trying to deduce the underground structure of an area. Weighted semblance can be used for increasing the resolution of traditional semblance or make traditional semblance capable of analyzing more complicated seismic data. == History == Semblance analysis is a technique that first began to be developed and used in the late 1960s. Prior to the discovery of this method, identifying the main reflections produced by the many layers under the ground was fairly difficult. The primary reflections of these layers were often obscured by the background noise as well as noise from the many secondary reflections that are produced. The use of semblance analysis allows for the removal of the extra noise and leaves only the primary reflection. == Process == Semblance analysis allows for the refinement of seismic data. This is done by developing a velocity spectra display to determine the velocity through different layers


id: sequence-analysis-in-social-sciences-20260505-0
file: data/en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences-0.md
category: reference
title: Sequence analysis in social sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences
date_saved: 2026-05-05T09:55:08.316729+00:00
instance: kb-cron
parent: sequence-analysis-in-social-sciences-20260505-0
summary:
In social sciences, sequence analysis (SA) is concerned with the analysis of sets of categorical sequences that typically describe longitudinal data. Analyzed sequences are encoded representations of, for example, individual life trajectories such as family formation, school to work transitions, working careers, but they may also describe daily or weekly time use or represent the evolution of observed or self-reported health, of political behaviors, or the development stages of organizations. Such sequences are chronologically ordered unlike words or DNA sequences for example. SA is a longitudinal analysis approach that is holistic in the sense that it considers each sequence as a whole. SA is essentially exploratory. Broadly, SA provides a comprehensible overall picture of sets of sequences with the objective of characterizing the structure of the set of sequences, finding the salient characteristics of groups, identifying typical paths, comparing groups, and more generally studying how the sequences are related to covariates such as sex, birth cohort, or social origin. Introduced in the social sciences in the 1980s by Andrew Abbott, SA has gained much popularity after the release of dedicated software such as the SQ and SADI addons for Stata and the TraMineR R package with its companions TraMineRextras and


id: sequence-analysis-in-social-sciences-20260505-1
file: data/en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences-1.md
category: reference
title: Sequence analysis in social sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences
date_saved: 2026-05-05T09:55:08.316729+00:00
instance: kb-cron
parent: sequence-analysis-in-social-sciences-20260505-0
summary:
=== Sociology === The analysis of sequence patterns has foundations in sociological theories that emerged in the middle of the 20th century. Structural theorists argued that society is a system that is characterized by regular patterns. Even seemingly trivial social phenomena are ordered in highly predictable ways. This idea serves as an implicit motivation behind social sequence analysts' use of optimal matching, clustering, and related methods to identify common "classes" of sequences at all levels of social organization, a form of pattern search. This focus on regularized patterns of social action has become an increasingly influential framework for understanding microsocial interaction and contact sequences, or "microsequences." This is closely related to Anthony Giddens's theory of structuration, which holds that social actors' behaviors are predominantly structured by routines, and which in turn provides predictability and a sense of stability in an otherwise chaotic and rapidly moving social world. This idea is also echoed in Pierre Bourdieu's concept of habitus, which emphasizes the emergence and influence of stable worldviews in guiding everyday action and thus produce predictable, orderly sequences of behavior. The resulting influence of routine as a structuring influence on social phenomena was first illustrated empirically by Pitirim Sorokin, who led


id: sequence-analysis-in-social-sciences-20260505-2
file: data/en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences-2.md
category: reference
title: Sequence analysis in social sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences
date_saved: 2026-05-05T09:55:08.316729+00:00
instance: kb-cron
parent: sequence-analysis-in-social-sciences-20260505-0
summary:
=== Political sciences === The analysis of temporal processes in the domain of political sciences regards how institutions, that is, systems and organizations (regimes, governments, parties, courts, etc.) that crystallize political interactions, formalize legal constraints and impose a degree of stability or inertia. Special importance is given to, first, the role of contexts, which confer meaning to trends and events, while shared contexts offer shared meanings; second, to changes over time in power relationships, and, subsequently, asymmetries, hierarchies, contention, or conflict; and, finally, to historical events that are able to shape trajectories, such as elections, accidents, inaugural speeches, treaties, revolutions, or ceasefires. Empirically, political sequences' unit of analysis can be individuals, organizations, movements, or institutional processes. Depending on the unit of analysis, the sample sizes may be limited few cases (e.g., regions in a country when considering the turnover of local political parties over time) or include a few hundreds (e.g., individuals' voting patterns). Three broad kinds of political sequences may be distinguished. The first and most common is careers, that is, formal, mostly hierarchical positions along which individuals progress in institutional environments, such as parliaments, cabinets, administrations, parties, unions or business organizations. We may name trajectories political sequences that


id: sequence-analysis-in-social-sciences-20260505-3
file: data/en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences-3.md
category: reference
title: Sequence analysis in social sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences
date_saved: 2026-05-05T09:55:08.316729+00:00
instance: kb-cron
parent: sequence-analysis-in-social-sciences-20260505-0
summary:
=== Visualization === State sequences can nicely be rendered graphically and such plots prove useful for interpretation purposes. As shown above, the two basic plots are the index plot that renders individual sequences and the chronogram that renders the evolution of the cross-sectional state distribution along the timeframe. Chronograms (also known as status proportion plot or state distribution plot) completely overlook the diversity of the sequences, while index plots are often too scattered to be readable. Relative frequency plots and plots of representative sequences attempt to increase the readability of index plots without falling in the oversimplification of a chronogram. In addition, there are many plots that focus on specific characteristics of the sequences. Below is a list of plots that have been proposed in the literature for rendering large sets of sequences. For each plot, we give examples of software (details in section Software) that produce it. Index plot: renders the set of individual sequences (SADI, SQ, TraMineR) Chronogram (status proportion plot, state distribution plot): renders the sequence of cross-sectional distributions (SADI, SQ, TraMineR) Plot of multidomain/multichannel sequences grouped by channels (TraMineR, seqHMM) or by individuals Plot of time series of cross-sectional indicators (entropy, modal state, ...) (SQ, TraMineR)


id: sequence-analysis-in-social-sciences-20260505-4
file: data/en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences-4.md
category: reference
title: Sequence analysis in social sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sequence_analysis_in_social_sciences
date_saved: 2026-05-05T09:55:08.316729+00:00
instance: kb-cron
parent: sequence-analysis-in-social-sciences-20260505-0
summary:
== Fields of application == These techniques have proved valuable in a variety of contexts. In life-course research, for example, research has shown that retirement plans are affected not just by the last year or two of one's life, but instead how one's work and family careers unfolded over a period of several decades. People who followed an "orderly" career path (characterized by consistent employment and gradual ladder-climbing within a single organization) retired earlier than others, including people who had intermittent careers, those who entered the labor force late, as well as those who enjoyed regular employment but who made numerous lateral moves across organizations throughout their careers. In the field of economic sociology, research has shown that firm performance depends not just on a firm's current or recent social network connectedness, but also the durability or stability of their connections to other firms. Firms that have more "durably cohesive" ownership network structures attract more foreign investment than less stable or poorly connected structures. Research has also used data on everyday work activity sequences to identify classes of work schedules, finding that the timing of work during the day significantly affects workers' abilities to maintain connections with the broader community,


id: seriation-statistics-20260505-0
file: data/en.wikipedia.org/wiki/Seriation_(statistics)-0.md
category: reference
title: Seriation (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Seriation_(statistics)
date_saved: 2026-05-05T09:55:09.506205+00:00
instance: kb-cron
parent: seriation-statistics-20260505-0
summary:
In combinatorial data analysis, seriation is the process of finding an arrangement of all objects in a set, in a linear order, given a loss function. The main goal is exploratory, to reveal structural information. == References ==


id: sesri-20260505-0
file: data/en.wikipedia.org/wiki/SESRI-0.md
category: reference
title: SESRI
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SESRI
date_saved: 2026-05-05T09:55:10.664517+00:00
instance: kb-cron
parent: sesri-20260505-0
summary:
Social and Economic Survey Research Institute (SESRI) is a survey institute and social science research contributor that is part of Qatar University. Qatar University realized the importance to enhance its social science research capacity with particular focus on monitoring the reality of national development and trends of its indicators based on evidence. Therefore, QU established SESRI in October 2008. To advance evidence-based decision-making and policy-making, SESRI established Policy Department in 2014. Since its commencement, SESRI carried out more than 100 high-quality surveys on Qataris citizens, and also on the enormous expatriate residents of Qatar. Surveys were conducted to guide the public and the government, in line with the Qatar National Vision 2030. All surveys were performed in accordance with the highest scientific and ethical standards. == SESRI Departments == SESRI has three departments: Survey Operations Support Department Research Department Policy Department == Accreditation == To ensure commitments to quality and to affirm compliance to international standards, SESRI successfully passed the requirements for ISO 9001:2015 Quality Management Standards from Bureau Veritas. == Key Roles == Provides consultancy services in survey research and socio-cultural development. Advances the evidence-based decision-making and policy-making. Capacity building for Qatari nationals in survey research methodology. Research topics


id: single-particle-trajectory-20260505-0
file: data/en.wikipedia.org/wiki/Single-particle_trajectory-0.md
category: reference
title: Single-particle trajectory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Single-particle_trajectory
date_saved: 2026-05-05T09:55:11.886018+00:00
instance: kb-cron
parent: single-particle-trajectory-20260505-0
summary:
Single-particle trajectories (SPTs) consist of a collection of successive discrete points causal in time. These trajectories are acquired from images in experimental data. In the context of cell biology, the trajectories are obtained by the transient activation by a laser of small dyes attached to a moving molecule. Molecules can now by visualized based on recent super-resolution microscopy, which allow routine collections of thousands of short and long trajectories. These trajectories explore part of a cell, either on the membrane or in 3 dimensions and their paths are critically influenced by the local crowded organization and molecular interaction inside the cell, as emphasized in various cell types such as neuronal cells, astrocytes, immune cells and many others. == SPTs allow observing moving molecules inside cells to collect statistics == SPT allowed observing moving particles. These trajectories are used to investigate cytoplasm or membrane organization, but also the cell nucleus dynamics, remodeler dynamics or mRNA production. Due to the constant improvement of the instrumentation, the spatial resolution is continuously decreasing, reaching now values of approximately 20 nm, while the acquisition time step is usually in the range of 10 to 50 ms to capture short events occurring in live tissues. A


id: single-particle-trajectory-20260505-1
file: data/en.wikipedia.org/wiki/Single-particle_trajectory-1.md
category: reference
title: Single-particle trajectory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Single-particle_trajectory
date_saved: 2026-05-05T09:55:11.886018+00:00
instance: kb-cron
parent: single-particle-trajectory-20260505-0
summary:
γ x ˙ + U ′ ( x ) = 2 ε γ w ˙ , {\displaystyle \gamma {\dot {x}}+U^{\prime }(x)={\sqrt {2\varepsilon \gamma }}\,{\dot {w}},} where w ˙ ( t ) {\displaystyle {\dot {w}}(t)} is δ {\displaystyle \delta } -correlated. This equation is obtained when the diffusion coefficient is constant in space. When this is not case, coarse grained equations (at a coarse spatial resolution) should be derived from molecular considerations. Interpretation of the physical forces are not resolved by Ito's vs Stratonovich integral representations or any others. === General model equations === For a timescale much longer than the elementary molecular collision, the position of a tracked particle is described by a more general overdamped limit of the Langevin stochastic model. Indeed, if the acquisition timescale of empirical recorded trajectories is much lower compared to the thermal fluctuations, rapid events are not resolved in the data. Thus at this coarser spatiotemporal scale, the motion description is replaced by an effective stochastic equation X ˙ ( t ) = b ( X ( t ) ) + 2 B e ( X ( t ) ) w ˙ ( t ) , ( 1 ) {\displaystyle {\dot {X}}(t)={b}(X(t))+{\sqrt {2}}{B}_{e}(X(t)){\dot {w}}(t),\qquad


id: single-particle-trajectory-20260505-2
file: data/en.wikipedia.org/wiki/Single-particle_trajectory-2.md
category: reference
title: Single-particle trajectory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Single-particle_trajectory
date_saved: 2026-05-05T09:55:11.886018+00:00
instance: kb-cron
parent: single-particle-trajectory-20260505-0
summary:
a x ( x k ) ≈ 1 N k ∑ j = 1 N t ∑ i = 0 , x ~ i j ∈ S ( x k , r ) N s − 1 ( x i + 1 j − x i j Δ t ) {\displaystyle a_{x}(x_{k})\approx {\frac {1}{N_{k}}}\sum _{j=1}^{N_{t}}\sum _{i=0,{\tilde {x}}_{i}^{j}\in S(x_{k},r)}^{N_{s}-1}\left({\frac {x_{i+1}^{j}-x_{i}^{j}}{\Delta t}}\right)} a y ( x k ) ≈ 1 N k ∑ j = 1 N t ∑ i = 0 , x ~ i j ∈ S ( x k , r ) N s − 1 ( y i + 1 j − y i j Δ t ) , {\displaystyle a_{y}(x_{k})\approx {\frac {1}{N_{k}}}\sum _{j=1}^{N_{t}}\sum _{i=0,{\tilde {x}}_{i}^{j}\in S(x_{k},r)}^{N_{s}-1}\left({\frac {y_{i+1}^{j}-y_{i}^{j}}{\Delta t}}\right),} where N k {\displaystyle N_{k}} is the number of points of trajectory that fall in the square S ( x k , r ) {\displaystyle S(x_{k},r)} . Similarly, the components of the effective diffusion tensor D ( x k ) {\displaystyle D(x_{k})} are approximated by the empirical sums D x x ( x k ) ≈ 1 N k ∑ j = 1 N t ∑ i = 0 , x i ∈ S ( x k , r


id: social-media-mining-20260505-0
file: data/en.wikipedia.org/wiki/Social_media_mining-0.md
category: reference
title: Social media mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_media_mining
date_saved: 2026-05-05T09:55:13.117723+00:00
instance: kb-cron
parent: social-media-mining-20260505-0
summary:
Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services. Social media mining uses concepts from computer science, data mining, machine learning, and statistics. Mining is based on social network analysis, network science, sociology, ethnography, optimization and mathematics. It attempts to formally represent, measure and model patterns from social media data. In the 2010s, major corporations, governments and not-for-profit organizations began mining to learn about customers, clients and others. Platforms such as Google, Facebook (partnered with Datalogix and BlueKai) conduct mining to target users


id: social-media-mining-20260505-1
file: data/en.wikipedia.org/wiki/Social_media_mining-1.md
category: reference
title: Social media mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_media_mining
date_saved: 2026-05-05T09:55:13.117723+00:00
instance: kb-cron
parent: social-media-mining-20260505-0
summary:
== Perception == Modern social media mining is a controversial practice that has led to exponential gains in user growth for tech giants such as Facebook, Inc., Twitter, and Google. Companies such as these, considered "Big Tech" are companies that build algorithms that take advantage of user input to understand their preferences, and keep them on the platform as much as possible. These inputs, that can be as simple as time spent on a given screen, provide the data being mined, and lead to companies profiting heavily from using that data to capitalize on extremely accurate predictions about user behavior. The growth of platforms accelerated rapidly once these strategies were put in place; Most of the largest platforms now average over 1 billion active users per month as of 2021. It has been claimed by a multitude of anti-algorithm personalities, like Tristan Harris or Chamath Palihapitiya, that certain companies (specifically Facebook) valued growth above all else, and ignored potential negative impacts from these growth engineering tactics. At the same time, users have now created their own data arbitrages with the help of their own data, through content monetization and becoming influencers. Users typically have access to a varied set of


id: social-media-mining-20260505-2
file: data/en.wikipedia.org/wiki/Social_media_mining-2.md
category: reference
title: Social media mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Social_media_mining
date_saved: 2026-05-05T09:55:13.117723+00:00
instance: kb-cron
parent: social-media-mining-20260505-0
summary:
KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining WWW Conference – International World Wide Web Conference WSDM Conference – ACM Conference on Web Search and Data Mining CIKM Conference – ACM Conference on Information and Knowledge Management ICDM Conference – IEEE International Conference on Data Mining Association for Computational Linguistics (ACL) ASONAM conference - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Internet Measuring Conference (IMC) International Conference on Web and Social Media (ICWSM) International Conference on Social Media & Society International Conference on Web Engineering (ICWE) The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML/PKDD), International Joint Conferences on Artificial Intelligence (IJCAI), Association for the Advancement of Artificial Intelligence (AAAI), Recommender Systems (RecSys) Computer-Human Interaction (CHI) Social Computing Behavioral-Cultural Modeling and Prediction (SBP). HT Conference – ACM Conference on Hypertext SDM Conference – SIAM International Conference on Data Mining (SIAM) PAKDD Conference – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining MISNC - Multidisciplinary International Social Networks Conference ==== Journals ==== DMKD Conference – Research Issues on Data Mining and Knowledge Discovery ECML-PKDD Conference – European Conference on Machine Learning and Principles and Practice


id: subdivision-bifiltration-20260505-0
file: data/en.wikipedia.org/wiki/Subdivision_bifiltration-0.md
category: reference
title: Subdivision bifiltration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Subdivision_bifiltration
date_saved: 2026-05-05T09:55:14.286535+00:00
instance: kb-cron
parent: subdivision-bifiltration-20260505-0
summary:
In topological data analysis, a subdivision bifiltration is a collection of filtered simplicial complexes, typically built upon a set of data points in a metric space, that captures shape and density information about the underlying data set. The subdivision bifiltration relies on a natural filtration of the barycentric subdivision of a simplicial complex by flags of minimum dimension, which encodes density information about the metric space upon which the complex is built. The subdivision bifiltration was first introduced by Donald Sheehy in 2011 as part of his doctoral thesis (later subsumed by a conference paper in 2012) as a discrete model of the multicover bifiltration, a continuous construction whose underlying framework dates back to the 1970s. In particular, Sheehy applied the construction to both the Vietoris-Rips and Čech filtrations, two common objects in the field of topological data analysis. Whereas single parameter filtrations are not robust with respect to outliers in the data, the subdivision-Rips and -Cech bifiltrations satisfy several desirable stability properties. == Definition == Let T {\displaystyle T} be a simplicial complex. Then a nested sequence of simplices σ 1 ⊂ σ 2 ⊂ ⋯ ⊂ σ k {\displaystyle \sigma _{1}\subset \sigma _{2}\subset \cdots \subset \sigma _{k}} of


id: symbolic-data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Symbolic_data_analysis-0.md
category: reference
title: Symbolic data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Symbolic_data_analysis
date_saved: 2026-05-05T09:55:15.432627+00:00
instance: kb-cron
parent: symbolic-data-analysis-20260505-0
summary:
Symbolic data analysis (SDA) is an extension of standard data analysis where symbolic data tables are used as input and symbolic objects are made output as a result. The data units are called symbolic since they are more complex than standard ones, as they not only contain values or categories, but also include internal variation and structure. SDA is based on four spaces: the space of individuals, the space of concepts, the space of descriptions, and the space of symbolic objects. The space of descriptions models individuals, while the space of symbolic objects models concepts. == References == == Further reading == Diday, Edwin; Noirhomme-Fraiture, Monique (2008). Symbolic Data Analysis and the SODAS Software. Wiley–Blackwell. ISBN 9780470018835. == External links == Symbolic Data Analysis: Conceptual Statistics and Data Mining An introduction to symbolic data analysis and its Application to the Sodas Project by Edwin Diday R2S: An R package to transform relational data into symbolic data


id: task-force-on-process-mining-20260505-0
file: data/en.wikipedia.org/wiki/Task_Force_on_Process_Mining-0.md
category: reference
title: Task Force on Process Mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Task_Force_on_Process_Mining
date_saved: 2026-05-05T09:55:16.578150+00:00
instance: kb-cron
parent: task-force-on-process-mining-20260505-0
summary:
The IEEE Task Force on Process Mining (TFPM) is a non-commercial association for process mining. The IEEE (Institute of Electrical and Electronics Engineers) Task Force on Process Mining was established in October 2009 as part of the IEEE Computational Intelligence Society at the Eindhoven University of Technology. The task force is supported by over 80 organizations and has around 750 members. The main goal of the task force is to promote the research, development, education, and understanding of process mining. == About == In 2012, the IEEE World Congress on Computational Intelligence/ IEEE Congress on Evolutionary Computation held a session on Process Mining. Process mining is a type of research that is a mix of computational intelligence and data mining, as well as process modeling and analysis. === Activities and organization === The Task Force on Process Mining has a Steering Committee and an Advisory Board. The Steering Committee, was chaired by Wil van der Aalst in its inception in 2009, defined 15 action lines. These include the organization of the annual International Process Mining Conference (ICPM) series, standardization efforts leading to the IEEE XES standard for storing and exchanging event data, and the Process Mining Manifesto which was translated


id: topological-data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Topological_data_analysis-0.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology. Extraction of information from datasets that are high-dimensional, incomplete and noisy is generally challenging. TDA provides a general framework to analyze such data in a manner that is insensitive to the particular metric chosen and provides dimensionality reduction and robustness to noise. Beyond this, it inherits functoriality, a fundamental concept of modern mathematics, from its topological nature, which allows it to adapt to new mathematical tools. The initial motivation is to study the shape of data. TDA has combined algebraic topology and other tools from pure mathematics to allow mathematically rigorous study of "shape". The main tool is persistent homology, an adaptation of homology to point cloud data. Persistent homology has been applied to many types of data across many fields. Moreover, its mathematical foundation is also of theoretical importance. The unique features of TDA make it a promising bridge between topology and geometry. == Basic theory == === Intuition === TDA is premised on the idea that the shape of data sets contains relevant information. Real high-dimensional data is typically sparse, and tends to have relevant low dimensional features. One


id: topological-data-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Topological_data_analysis-1.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
=== Concepts === Some widely used concepts are introduced below. Note that some definitions may vary from author to author. A point cloud is often defined as a finite set of points in some Euclidean space, but may be taken to be any finite metric space. The Čech complex of a point cloud is the nerve of the cover of balls of a fixed radius around each point in the cloud. A persistence module U {\displaystyle \mathbb {U} } indexed by Z {\displaystyle \mathbb {Z} } is a vector space U t {\displaystyle U_{t}} for each t ∈ Z {\displaystyle t\in \mathbb {Z} } , and a linear map u t s : U s → U t {\displaystyle u_{t}^{s}\colon U_{s}\to U_{t}} whenever s ≤ t {\displaystyle s\leq t} , such that u t t = 1 {\displaystyle u_{t}^{t}=1} for all t {\displaystyle t} and u t s u s r = u t r {\displaystyle u_{t}^{s}u_{s}^{r}=u_{t}^{r}} whenever r ≤ s ≤ t . {\displaystyle r\leq s\leq t.} An equivalent definition is a functor from Z {\displaystyle \mathbb {Z} } considered as a partially ordered set to the category of vector spaces. The persistent homology group P H {\displaystyle PH}


id: topological-data-analysis-20260505-2
file: data/en.wikipedia.org/wiki/Topological_data_analysis-2.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
== Computation == The first algorithm over all fields for persistent homology in algebraic topology setting was described by Barannikov through reduction to the canonical form by upper-triangular matrices. The algorithm for persistent homology over F 2 {\displaystyle F_{2}} was given by Edelsbrunner et al. Afra Zomorodian and Carlsson gave the practical algorithm to compute persistent homology over all fields. Edelsbrunner and Harer's book gives general guidance on computational topology. One issue that arises in computation is the choice of complex. The Čech complex and the Vietoris–Rips complex are most natural at first glance; however, their size grows rapidly with the number of data points. The Vietoris–Rips complex is preferred over the Čech complex because its definition is simpler and the Čech complex requires extra effort to define in a general finite metric space. Efficient ways to lower the computational cost of homology have been studied. For example, the α-complex and witness complex are used to reduce the dimension and size of complexes. Recently, Discrete Morse theory has shown promise for computational homology because it can reduce a given simplicial complex to a much smaller cellular complex which is homotopic to the original one. This reduction can in fact be


id: topological-data-analysis-20260505-3
file: data/en.wikipedia.org/wiki/Topological_data_analysis-3.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
== Multidimensional persistence == Multidimensional persistence is important to TDA. The concept arises in both theory and practice. The first investigation of multidimensional persistence was early in the development of TDA. Carlsson-Zomorodian introduced the theory of multidimensional persistence in and in collaboration with Singh introduced the use of tools from symbolic algebra (Grobner basis methods) to compute MPH modules. Their definition presents multidimensional persistence with n parameters as a Z n {\displaystyle \mathbb {Z} ^{n}} graded module over a polynomial ring in n variables. Tools from commutative and homological algebra are applied to the study of multidimensional persistence in work of Harrington-Otter-Schenck-Tillman. The first application to appear in the literature is a method for shape comparison, similar to the invention of TDA. The definition of an n-dimensional persistence module in R n {\displaystyle \mathbb {R} ^{n}} is vector space V s {\displaystyle V_{s}} is assigned to each point in s = ( s 1 , … , s n ) {\displaystyle s=(s_{1},\ldots ,s_{n})} map ρ s t : V s → V t {\displaystyle \rho _{s}^{t}\colon V_{s}\to V_{t}} is assigned if s ≤ t {\displaystyle s\leq t} ( s i ≤ t i , i = 1 , … ,


id: topological-data-analysis-20260505-4
file: data/en.wikipedia.org/wiki/Topological_data_analysis-4.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
=== Extended persistence and levelset persistence === There are some attempts to loosen the stricter restriction of the function. Please refer to the Categorification and cosheaves and Impact on mathematics sections for more information. It's natural to extend persistence homology to other basic concepts in algebraic topology, such as cohomology and relative homology/cohomology. An interesting application is the computation of circular coordinates for a data set via the first persistent cohomology group. === Circular persistence === Normal persistence homology studies real-valued functions. The circle-valued map might be useful, "persistence theory for circle-valued maps promises to play the role for some vector fields as does the standard persistence theory for scalar fields", as commented in Dan Burghelea et al. The main difference is that Jordan cells (very similar in format to the Jordan blocks in linear algebra) are nontrivial in circle-valued functions, which would be zero in real-valued case, and combining with barcodes give the invariants of a tame map, under moderate conditions. Two techniques they use are Morse-Novikov theory and graph representation theory. More recent results can be found in D. Burghelea et al. For example, the tameness requirement can be replaced by the much weaker condition, continuous. === Persistence


id: topological-data-analysis-20260505-5
file: data/en.wikipedia.org/wiki/Topological_data_analysis-5.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
The soft stability theorem asserts that H F {\displaystyle HF} is Lipschitz continuous, and the hard stability theorem asserts that J {\displaystyle J} is Lipschitz continuous. Bottleneck distance is widely used in TDA. The isometry theorem asserts that the interleaving distance d I {\displaystyle d_{I}} is equal to the bottleneck distance. Bubenik et al. have abstracted the definition to that between functors F , G : P → D {\displaystyle F,G\colon P\to D} when P {\textstyle P} is equipped with a sublinear projection or superlinear family, in which still remains a pseudometric. Considering the magnificent characters of interleaving distance, here we introduce the general definition of interleaving distance(instead of the first introduced one): Let Γ , K ∈ T r a n s P {\displaystyle \Gamma ,K\in \mathrm {Trans_{P}} } (a function from P {\textstyle P} to P {\textstyle P} which is monotone and satisfies x ≤ Γ ( x ) {\displaystyle x\leq \Gamma (x)} for all x ∈ P {\textstyle x\in P} ). A ( Γ , K ) {\displaystyle (\Gamma ,K)} -interleaving between F and G consists of natural transformations φ : F ⇒ G Γ {\displaystyle \varphi \colon F\Rightarrow G\Gamma } and ψ : G ⇒ F


id: topological-data-analysis-20260505-6
file: data/en.wikipedia.org/wiki/Topological_data_analysis-6.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
== Structure theorem == The structure theorem is of central importance to TDA; as commented by G. Carlsson, "what makes homology useful as a discriminator between topological spaces is the fact that there is a classification theorem for finitely generated abelian groups". (see the fundamental theorem of finitely generated abelian groups). The main argument used in the proof of the original structure theorem is the standard structure theorem for finitely generated modules over a principal ideal domain. However, this argument fails if the indexing set is ( R , ≤ ) {\displaystyle (\mathbb {R} ,\leq )} . In general, not every persistence module can be decomposed into intervals. Many attempts have been made at relaxing the restrictions of the original structure theorem. The case for pointwise finite-dimensional persistence modules indexed by a locally finite subset of R {\displaystyle \mathbb {R} } is solved based on the work of Webb. The most notable result is done by Crawley-Boevey, which solved the case of R {\displaystyle \mathbb {R} } . Crawley-Boevey's theorem states that any pointwise finite-dimensional persistence module is a direct sum of interval modules. To understand the definition of his theorem, some concepts need introducing. An interval in ( R


id: topological-data-analysis-20260505-7
file: data/en.wikipedia.org/wiki/Topological_data_analysis-7.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
== Statistics == Real data is always finite, and so its study requires us to take stochasticity into account. Statistical analysis gives us the ability to separate true features of the data from artifacts introduced by random noise. Persistent homology has no inherent mechanism to distinguish between low-probability features and high-probability features. One way to apply statistics to topological data analysis is to study the statistical properties of topological features of point clouds. The study of random simplicial complexes offers some insight into statistical topology. Katharine Turner et al. offers a summary of work in this vein. A second way is to study probability distributions on the persistence space. The persistence space B ∞ {\displaystyle B_{\infty }} is ∐ n B n / ∽ {\displaystyle \coprod _{n}B_{n}/{\backsim }} , where B n {\displaystyle B_{n}} is the space of all barcodes containing exactly n {\displaystyle n} intervals and the equivalences are { [ x 1 , y 1 ] , [ x 2 , y 2 ] , … , [ x n , y n ] } ∽ { [ x 1 , y 1 ] , [ x 2 , y 2 ] , … , [ x n


id: topological-data-analysis-20260505-8
file: data/en.wikipedia.org/wiki/Topological_data_analysis-8.md
category: reference
title: Topological data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Topological_data_analysis
date_saved: 2026-05-05T09:55:17.950679+00:00
instance: kb-cron
parent: topological-data-analysis-20260505-0
summary:
=== Classification of applications === More than one way exists to classify the applications of TDA. Perhaps the most natural way is by field. A very incomplete list of successful applications includes data skeletonization, shape study, graph reconstruction, image analysis, material, progression analysis of disease, sensor network, signal analysis, cosmic web, complex network, fractal geometry, viral evolution, propagation of contagions on networks, bacteria classification using molecular spectroscopy, super-resolution microscopy, hyperspectral imaging in physical-chemistry, remote sensing, feature selection, and early warning signs of financial crashes. Another way is by distinguishing the techniques by G. Carlsson,one being the study of homological invariants of data on individual data sets, and the other is the use of homological invariants in the study of databases where the data points themselves have geometric structure. === Impact on mathematics === Topological data analysis and persistent homology have had impacts on Morse theory. Morse theory has played a very important role in the theory of TDA, including on computation. Some work in persistent homology has extended results about Morse functions to tame functions or, even to continuous functions. A forgotten result of R. Deheuvels long before the invention of persistent homology extends Morse theory to all continuous functions.


id: twyman-s-law-20260505-0
file: data/en.wikipedia.org/wiki/Twyman's_law-0.md
category: reference
title: Twyman's law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Twyman's_law
date_saved: 2026-05-05T09:55:19.200428+00:00
instance: kb-cron
parent: twyman-s-law-20260505-0
summary:
Twyman's law states that "Any figure that looks interesting or different is usually wrong", following the principle that "the more unusual or interesting the data, the more likely they are to have been the result of an error of one kind or another". It is named after the media and market researcher Tony Twyman and has been described as one of the most important laws of data analysis. The law is based on the fact that errors in data measurement and analysis can lead to observed quantities that are wildly different from typical values. These errors are usually more common than real changes of similar magnitude in the underlying process being measured. For example, if an analyst at a software company notices that the number of users has doubled overnight, the most likely explanation is a bug in logging, rather than a true increase in users. The law can also be extended to situations where the underlying data is influenced by unexpected factors that differ from what was intended to be measured. For example, when schools show unusually large improvements in test scores, subsequent investigation often reveals that those scores were driven by fraud. == See also == Extraordinary claims


id: unicity-data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Unicity_(data_analysis)-0.md
category: reference
title: Unicity (data analysis)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Unicity_(data_analysis)
date_saved: 2026-05-05T09:55:20.371242+00:00
instance: kb-cron
parent: unicity-data-analysis-20260505-0
summary:
Unicity ( ε p {\displaystyle \varepsilon _{p}} ) is a risk metric for measuring the re-identifiability of high-dimensional anonymous data. First introduced in 2013, unicity is measured by the number of points p needed to uniquely identify an individual in a data set. The fewer points needed, the more unique the traces are and the easier they would be to re-identify using outside information. In a high-dimensional, human behavioural data set, such as mobile phone meta-data, for each person, there exists potentially thousands of different records. In the case of mobile phone meta-data, credit card transaction histories and many other types of personal data, this information includes the time and location of an individual. In research, unicity is widely used to illustrate the re-identifiability of anonymous data sets. In 2013 researchers from the MIT Media Lab showed that only 4 points needed to uniquely identify 95% of individual trajectories in a de-identified data set of 1.5 million mobility trajectories. These points were location-time pairs that appeared with the resolution of 1 hour and 0.15 km² to 15 km². These results were shown to hold true for credit card transaction data as well with 4 points being enough to re-identify 90%


id: vietoris-rips-filtration-20260505-0
file: data/en.wikipedia.org/wiki/Vietoris–Rips_filtration-0.md
category: reference
title: Vietoris–Rips filtration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Vietoris–Rips_filtration
date_saved: 2026-05-05T09:55:21.553662+00:00
instance: kb-cron
parent: vietoris-rips-filtration-20260505-0
summary:
In topological data analysis, the Vietoris–Rips filtration (sometimes shortened to "Rips filtration") is the collection of nested Vietoris–Rips complexes on a metric space created by taking the sequence of Vietoris–Rips complexes over an increasing scale parameter. Often, the Vietoris–Rips filtration is used to create a discrete, simplicial model on point cloud data embedded in an ambient metric space. The Vietoris–Rips filtration is a multiscale extension of the Vietoris–Rips complex that enables researchers to detect and track the persistence of topological features, over a range of parameters, by way of computing the persistent homology of the entire filtration. It is named after Leopold Vietoris and Eliyahu Rips. == Definition == The Vietoris–Rips filtration is the nested collection of Vietoris–Rips complexes indexed by an increasing scale parameter. The Vietoris–Rips complex is a classical construction in mathematics that dates back to a 1927 paper of Leopold Vietoris, though it was independently considered by Eliyahu Rips in the study of hyperbolic groups, as noted by Mikhail Gromov in the 1980s. The conjoined name "Vietoris–Rips" is due to Jean-Claude Hausmann. Given a metric space X {\displaystyle X} and a scale parameter (sometimes called the threshold or distance parameter) r ∈ [ 0 , ∞ )


id: vietoris-rips-filtration-20260505-1
file: data/en.wikipedia.org/wiki/Vietoris–Rips_filtration-1.md
category: reference
title: Vietoris–Rips filtration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Vietoris–Rips_filtration
date_saved: 2026-05-05T09:55:21.553662+00:00
instance: kb-cron
parent: vietoris-rips-filtration-20260505-0
summary:
== Properties == The size of a filtration refers to the number of simplices in the largest complex, assuming the underlying metric space is finite. The k {\displaystyle k} -skeleton, i.e., the number of simplices up to dimension k {\displaystyle k} , of the Vietoris–Rips filtration is known to be O ( n k + 1 ) {\displaystyle O\left(n^{k+1}\right)} , where n {\displaystyle n} is the number of points. The size of the complete skeleton has precisely 2 n − 1 {\displaystyle 2^{n}-1} simplices, one for each non-empty subset of points. Since this is exponential, researchers usually only compute the skeleton of the Vietoris–Rips filtration up to small values of k {\displaystyle k} . When the underlying metric space is finite, the Vietoris–Rips filtration is sometimes referred to as essentially discrete, meaning that there exists some terminal or maximum scale parameter r max ∈ [ 0 , ∞ ) {\displaystyle r_{\text{max}}\in [0,\infty )} such that V R s ( X ) = V R r max ( X ) {\displaystyle \mathbf {VR} _{s}(X)=\mathbf {VR} _{r_{\max }}(X)} for all s ≥ r max {\displaystyle s\geq r_{\max }} , and furthermore that the inclusion map V R s → t ( X


id: vietoris-rips-filtration-20260505-2
file: data/en.wikipedia.org/wiki/Vietoris–Rips_filtration-2.md
category: reference
title: Vietoris–Rips filtration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Vietoris–Rips_filtration
date_saved: 2026-05-05T09:55:21.553662+00:00
instance: kb-cron
parent: vietoris-rips-filtration-20260505-0
summary:
The Degree-Rips bifiltration extends the Vietoris–Rips filtration by constructing a sub-graph of the 1-skeleton of each complex in the Vietoris–Rips filtration, restricting only to vertices whose degree is at least a given parameter a ∈ [ 0 , ∞ ) {\displaystyle a\in [0,\infty )} , then building the clique complex on that subgraph. The degree of a vertex encodes density information about the data, because it is quantifies how "central" that point is by way of how many other vertices it is connected to. The collection over all degree parameters a {\displaystyle a} defines a filtration of each complex in the Vietoris–Rips filtration, where the complexes get smaller as a {\displaystyle a} increases. Altogether, this defines a 2-parameter extension of the Vietoris–Rips filtration, by considering the collection of bi-filtered complexes over all scale parameters ( a , r ) ∈ R op × R {\displaystyle (a,r)\in \mathbb {R} ^{\operatorname {op} }\times \mathbb {R} } , where "op" denotes the opposite poset. The Function-Rips bifiltration extends the Vietoris–Rips filtration by bifiltering each complex according to the superlevel-sets of some function γ : X → R {\displaystyle \gamma :X\to \mathbb {R} } , where γ {\displaystyle \gamma } can be a density


id: visual-inspection-20260505-0
file: data/en.wikipedia.org/wiki/Visual_inspection-0.md
category: reference
title: Visual inspection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Visual_inspection
date_saved: 2026-05-05T09:55:22.749258+00:00
instance: kb-cron
parent: visual-inspection-20260505-0
summary:
Visual inspection is a common method of quality control, data acquisition, and data analysis. Visual Inspection, used in maintenance of facilities, means inspection of equipment and structures using either or all of raw human senses such as vision, hearing, touch and smell and/or any non-specialized inspection equipment. Inspections requiring Ultrasonic, X-Ray equipment, Infrared, etc. are not typically regarded as visual inspection as these Inspection methodologies require specialized equipment, training and certification. == Quality control == A study of the visual inspection of small integrated circuits found that the modal duration of eye fixations of trained inspectors was about 200 ms. The most accurate inspectors made the fewest eye fixations and were the fastest. When the same chip was judged more than once by an individual inspector the consistency of judgment was very high whereas the consistency between inspectors was somewhat less. Variation by a factor of six in inspection speed led to variation of less than a factor of two in inspection accuracy. Visual inspection had a false positive rate of 2% and a false negative rate of 23%. == Humorous terminology == To do an eyeball search is to look for something specific in a mass of code or


id: chronological-dating-20260505-0
file: data/en.wikipedia.org/wiki/Chronological_dating-0.md
category: reference
title: Chronological dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Chronological_dating
date_saved: 2026-05-05T09:55:24.404740+00:00
instance: kb-cron
parent: chronological-dating-20260505-0
summary:
Chronological dating, or simply dating, is the process of attributing to an object or event a date in the past, allowing such object or event to be located in a previously established chronology. This usually requires what is commonly known as a "dating method". Several dating methods exist, depending on different criteria and techniques, and some very well known examples of disciplines using such techniques are, for example, history, geology, paleontology, archaeology, astronomy and even forensic science, since in the latter it is sometimes necessary to investigate the moment in the past during which the death of a cadaver occurred. These methods are typically identified as absolute, which involves a specified date or date range, or relative, which refers to dating which places artifacts or events on a timeline relative to other events and/or artifacts. Other markers can help place an artifact or event in a chronology, such as nearby writings and stratigraphic markers. == Absolute and relative dating == Dating methods are most commonly classified following two criteria: relative dating and absolute dating. === Relative dating === Relative dating methods are unable to determine the absolute age of an object or event, but can determine the impossibility of a


id: chronological-dating-20260505-1
file: data/en.wikipedia.org/wiki/Chronological_dating-1.md
category: reference
title: Chronological dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Chronological_dating
date_saved: 2026-05-05T09:55:24.404740+00:00
instance: kb-cron
parent: chronological-dating-20260505-0
summary:
== Dating methods in archaeology == Just like geologists or paleontologists, archaeologists are also brought to determine the age of both ancient and recent humans. Thus, to be considered as archaeological, the remains, objects or artifacts to be dated must be related to human activity. It is commonly assumed that if the remains or elements to be dated are older than the human species, the disciplines which study them are sciences such geology or paleontology, among some others. Nevertheless, the range of time within archaeological dating can be enormous compared to the average lifespan of a singular human being. As an example Pinnacle Point's caves, in the southern coast of South Africa, provided evidence that marine resources (shellfish) have been regularly exploited by humans as of 170,000 years ago. On the other hand, remains as recent as a hundred years old can also be the target of archaeological dating methods. It was the case of an 18th-century sloop whose excavation was led in South Carolina (United States) in 1992. Thus, from the oldest to the youngest, all archaeological sites are likely to be dated by an appropriate method. Dating material drawn from the archaeological record can be made by a


id: absolute-dating-20260505-0
file: data/en.wikipedia.org/wiki/Absolute_dating-0.md
category: reference
title: Absolute dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Absolute_dating
date_saved: 2026-05-05T09:55:25.571074+00:00
instance: kb-cron
parent: absolute-dating-20260505-0
summary:
Absolute dating is the process of determining an age on a specified chronology in archaeology and geology. Absolute dating provides a numerical age or range, in contrast with relative dating, which places events in order without any measure of the age between events. Some scientists prefer the terms chronometric dating or calendar dating, as the use of the word "absolute" may imply an unwarranted certainty of accuracy. In archaeology, absolute dating is usually based on the physical, chemical, and life properties of the materials of artifacts, buildings, or other items that have been modified by humans and by historical associations with materials with known dates (such as coins and historical records). For example, coins found in excavations may have their production date written on them, or there may be written records describing the coin and when it was used, allowing the site to be associated with a particular calendar year. Absolute dating techniques include radiocarbon dating of wood or bones, potassium-argon dating, and trapped-charge dating methods such as thermoluminescence dating of glazed ceramics. In historical geology, the primary methods of absolute dating involve using the radioactive decay of elements trapped in rocks or minerals, including isotope systems from younger organic


id: absolute-dating-20260505-1
file: data/en.wikipedia.org/wiki/Absolute_dating-1.md
category: reference
title: Absolute dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Absolute_dating
date_saved: 2026-05-05T09:55:25.571074+00:00
instance: kb-cron
parent: absolute-dating-20260505-0
summary:
=== Optically stimulated luminescence (OSL) === Optically stimulated luminescence (OSL) dating constrains the time at which sediment was last exposed to light. During sediment transport, exposure to sunlight 'zeros' the luminescence signal. Upon burial, the sediment accumulates a luminescence signal as natural ambient radiation gradually ionises the mineral grains. Careful sampling under dark conditions allows the sediment to be exposed to artificial light in the laboratory, which releases the OSL signal. The amount of luminescence released is used to calculate the equivalent dose (De) that the sediment has acquired since deposition, which can be used in combination with the dose rate (Dr) to calculate the age. == Dendrochronology == Dendrochronology, or tree-ring dating, is the scientific method of dating based on the analysis of patterns of tree rings, also known as growth rings. Dendrochronology can date the time at which tree rings were formed, in many types of wood, to the exact calendar year. Dendrochronology has three main areas of application: paleoecology, where it is used to determine certain aspects of past ecologies (most prominently climate); archaeology, where it is used to date old buildings, etc.; and radiocarbon dating, where it is used to calibrate radiocarbon ages (see below). In


id: acanthochronology-20260505-0
file: data/en.wikipedia.org/wiki/Acanthochronology-0.md
category: reference
title: Acanthochronology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Acanthochronology
date_saved: 2026-05-05T09:55:26.759970+00:00
instance: kb-cron
parent: acanthochronology-20260505-0
summary:
Acanthochronology is the study of cactus spines or Euphorbia thorns grown in time ordered sequence (i.e. in series). Physical, morphological or chemical characteristics and information about the relative order or absolute age of the spines or thorns is used to study past climate or plant physiology. For example, columnar cactus spines grow from the apex of the plant. After several weeks the spines stop growing and have been moved to the side of the stem. The old spines remain in place for decades as new spines are created at the continually growing apex. The result is that along each external "rib" of the cactus is a series of spines arranged in the order they grew in – the oldest spines are at the bottom and the youngest spines are at the top. These spines can be dated using bomb-spike Carbon-14 and isotopes of carbon (Carbon-13) and oxygen (Oxygen-18) may be used to infer past climate (e.g. precipitation or temperature), plant stem growth or plant physiology (e.g. photosynthetic processes). Alternatively, the width of small transverse bands in the spine may be used to infer daily information about cloud cover or plant productivity, although this remains to be tested. It has also


id: amino-acid-dating-20260505-0
file: data/en.wikipedia.org/wiki/Amino_acid_dating-0.md
category: reference
title: Amino acid dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Amino_acid_dating
date_saved: 2026-05-05T09:55:27.976616+00:00
instance: kb-cron
parent: amino-acid-dating-20260505-0
summary:
Amino acid dating or racemization dating is a dating technique used to estimate the age of a specimen in paleobiology, molecular paleontology, archaeology, forensic science, taphonomy, sedimentary geology and other fields. This technique relates changes in amino acid molecules to the time elapsed since they were formed. == Background == === Chemistry === Amino acids are a set of organic compounds that are used by living organisms to synthesise proteins. All amino acids (except glycine) have one or more pairs of stereoisomers, isomers which share the same bond order but are organized differently in 3D space. Amino acid stereoisomer pairs that are optically active and non-superimposable mirror images of each other are enantiomers; pairs that are not mirror images are diastereomers or epimers. Biological systems are stereoselective, preferring certain stereoisomers for chemical reactions; living organisms keep all their amino acids in their "left-handed" (L or levo-) forms (a state called homochirality) because they are unable to use the "right-handed" (D or dextro-) forms for protein synthesis. This ratio of D and L forms is unstable, as the molecules may undergo reactions (known as racemization or epimerization respective to the type of stereoisomer pair involved) and become the other stereoisomer. When


id: appearance-event-ordination-20260505-0
file: data/en.wikipedia.org/wiki/Appearance_event_ordination-0.md
category: reference
title: Appearance event ordination
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Appearance_event_ordination
date_saved: 2026-05-05T09:55:29.143585+00:00
instance: kb-cron
parent: appearance-event-ordination-20260505-0
summary:
Appearance event ordination or AEO is a scientific method for biochronology through the ordering of the appearance of fossil mammal genera by multivariate analysis, using conjunctional (overlapping) and disconjunctional (nonoverlapping) range distributions in large sets of data. == Process == AEO is based on faunal overlap and stratigraphic superposition to derive a best-fit sequence of first and last appearance events. === Step 1 === The first step is to translate patterns of overlap and superposition into pairwise first-before-last statements. The wolf species Canis edwardii and Canis armbrusteri are used as example taxa for the following patterns. Each statement means C. edwardii, for example, must have first appeared before C. armbusteri last appeared. This is true whenever either (1) C. edwardii and C. armbrusteri have been found together in at least one nontime-averaged fossil collection, or (2) C. edwardii is found lower in at least one lithostratigraphic section than C. armbrusteri. === Step 2 === A multivariate ordination algorithm is applied to derive a first-pass, hypothesized sequence of first and last appearances. The minimal constraint on this sequence is that if there is an observed, real-world C. edwardii before C. armbrusteri statement for any pair of taxa, the hypothesized event sequence


id: astronomical-chronology-20260505-0
file: data/en.wikipedia.org/wiki/Astronomical_chronology-0.md
category: reference
title: Astronomical chronology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Astronomical_chronology
date_saved: 2026-05-05T09:55:30.297138+00:00
instance: kb-cron
parent: astronomical-chronology-20260505-0
summary:
Astronomical chronology, or astronomical dating, is a technical method of dating events or artifacts that are associated with astronomical phenomena. Written records of historical events that include descriptions of astronomical phenomena have done much to clarify the chronology of the Ancient Near East; works of art which depict the configuration of the stars and planets and buildings which are oriented to the rising and setting of celestial bodies at a particular time have all been dated through astronomical calculations. == Dating historical events == The use of descriptions of astronomical phenomena to date historical events began in the 16th century, a time of a renewed humanistic interest in history and of increasingly precise astronomical tables. Eclipses in particular are relatively infrequent events and can be dated precisely. When the circumstances are not exact and descriptions leave ambiguities, one can often use other details such as the month of the eclipse or the position of other stars and planets to identify the specific eclipse. Astronomical dating, like other forms of historical interpretation, requires care in interpreting the surviving written records. John Steele has proposed three questions that must be asked when dating an event: Does the record refer to an actual


id: dendrochronology-20260505-0
file: data/en.wikipedia.org/wiki/Dendrochronology-0.md
category: reference
title: Dendrochronology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Dendrochronology
date_saved: 2026-05-05T09:55:31.464391+00:00
instance: kb-cron
parent: dendrochronology-20260505-0
summary:
Dendrochronology (or tree-ring dating) is the scientific method of dating tree rings (also called growth rings) to the exact year they were formed in a tree. As well as dating them, this can give data for dendroclimatology, the study of climate and atmospheric conditions during different periods in history from the wood of old trees. Dendrochronology derives from the Ancient Greek dendron (δένδρον), meaning "tree", khronos (χρόνος), meaning "time", and -logia (-λογία), "the study of". Dendrochronology is useful for determining the precise age of samples, especially those that are too recent for radiocarbon dating, which always produces a range rather than an exact date. However, for a precise date of the death of the tree a full sample to the edge is needed, which most trimmed timber will not provide. It also gives data on the timing of events and rates of change in the environment (most prominently climate) and also in wood found in archaeology or works of art and architecture, such as old panel paintings. It is also used as a check in radiocarbon dating to calibrate radiocarbon ages. New growth in trees occurs in a layer of cells near the bark. A tree's growth rate changes in


id: dendrochronology-20260505-1
file: data/en.wikipedia.org/wiki/Dendrochronology-1.md
category: reference
title: Dendrochronology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Dendrochronology
date_saved: 2026-05-05T09:55:31.464391+00:00
instance: kb-cron
parent: dendrochronology-20260505-0
summary:
Many trees in temperate zones produce one growth-ring each year, with the newest adjacent to the bark. Hence, for the entire period of a tree's life, a year-by-year record or ring pattern builds up that reflects the age of the tree and the climatic conditions in which the tree grew. Adequate moisture and a long growing season result in a wide ring, while a drought year may result in a very narrow one. Direct reading of tree ring chronologies is a complex science, for several reasons. First, contrary to the single-ring-per-year paradigm, alternating poor and favorable conditions, such as mid-summer droughts, can result in several rings forming in a given year. In addition, particular tree species may present "missing rings", and this influences the selection of trees for study of long time-spans. For instance, missing rings are rare in oak and elm trees. Critical to the science, trees from the same region tend to develop the same patterns of ring widths for a given period of chronological study. Researchers can compare and match these patterns ring-for-ring with patterns from trees which have grown at the same time in the same geographical zone (and therefore under similar climatic conditions). When one


id: dendrochronology-20260505-2
file: data/en.wikipedia.org/wiki/Dendrochronology-2.md
category: reference
title: Dendrochronology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Dendrochronology
date_saved: 2026-05-05T09:55:31.464391+00:00
instance: kb-cron
parent: dendrochronology-20260505-0
summary:
=== Miyake events === Miyake events, such as the ones in 774–775 and 993–994, can provide fixed reference points in an unknown time sequence as they are due to cosmic radiation. As they appear as spikes in carbon 14 in tree rings for that year all round the world, they can be used to date historical events to the year. For example, wooden houses in the Viking site at L'Anse aux Meadows in Newfoundland were dated by finding the layer with the 993 spike, which showed that the wood is from a tree felled in 1021. Researchers at the University of Bern have provided exact dating of a floating sequence in a Neolithic settlement in northern Greece by tying it to a spike in cosmogenic radiocarbon in 5259 BC. === Frost rings === Frost ring is a term used to designate a layer of deformed, collapsed tracheids and traumatic parenchyma cells in tree ring analysis. They are formed when air temperature falls below freezing during a period of cambial activity. They can be used in dendrochronology to indicate years that are colder than usual. == Applications == === Radiocarbon dating calibration === Dates from dendrochronology can be used as a


id: dendrochronology-20260505-3
file: data/en.wikipedia.org/wiki/Dendrochronology-3.md
category: reference
title: Dendrochronology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Dendrochronology
date_saved: 2026-05-05T09:55:31.464391+00:00
instance: kb-cron
parent: dendrochronology-20260505-0
summary:
The Post Track and Sweet Track, ancient timber trackways in the Somerset levels, England, have been dated to 3838 BC and 3807 BC. Navan Fort where in Prehistoric Ireland a large structure was built with more than two hundred posts. The central oak post was felled in 95 BC. The Fairbanks House in Dedham, Massachusetts. While the house had long been claimed to have been built c. 1640 (and being the oldest wood-framed house in North America), core samples of wood taken from a summer beam confirmed the wood was from an oak tree felled in 1637–8, as wood was not seasoned before use in building at that time in New England. An additional sample from another beam yielded a date of 1641, thus confirming the house had been constructed starting in 1638 and finished sometime after 1641 . The burial chamber of Gorm the Old, who died c. 958, was constructed from wood of timbers felled in 958. Veliky Novgorod, where, between the tenth and the fifteenth century, numerous consecutive layers of wooden log pavement have been placed over the accumulating dirt. The Neolithic well with linings made of oak wood, found near Ostrov, Czech Republic, have been dated


id: law-of-superposition-20260505-0
file: data/en.wikipedia.org/wiki/Law_of_superposition-0.md
category: reference
title: Law of superposition
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Law_of_superposition
date_saved: 2026-05-05T09:55:33.767014+00:00
instance: kb-cron
parent: law-of-superposition-20260505-0
summary:
The law of superposition is an axiom that forms one of the bases of the sciences of geology, archaeology, and other fields pertaining to geological stratigraphy. In its plainest form, it states that in undeformed stratigraphic sequences, the oldest strata will lie at the bottom of the sequence, while newer material stacks upon the surface to form new deposits over time. This is paramount to stratigraphic dating, which requires a set of assumptions, including that the law of superposition holds true and that an object cannot be older than the materials of which it is composed. To illustrate the practical applications of superposition in scientific inquiry, sedimentary rock that has not been deformed by more than 90° will exhibit the oldest layers on the bottom, thus enabling paleontologists and paleobotanists to identify the relative ages of any fossils found within the strata, with the remains of the most archaic lifeforms confined to the lowest. These findings can inform the community on the fossil record covering the relevant strata, to determine which species coexisted temporally and which species existed successively in perhaps an evolutionarily or phylogenetically relevant way. == History == The law of superposition was first proposed in 1669 by


id: marine-isotope-stages-20260505-0
file: data/en.wikipedia.org/wiki/Marine_isotope_stages-0.md
category: reference
title: Marine isotope stages
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Marine_isotope_stages
date_saved: 2026-05-05T09:55:34.928910+00:00
instance: kb-cron
parent: marine-isotope-stages-20260505-0
summary:
Marine isotope stages (MIS), marine oxygen-isotope stages, or oxygen isotope stages (OIS), are alternating warm and cool periods in the Earth's paleoclimate, deduced from oxygen isotope data derived from deep sea core samples. Working backwards from the present, which is MIS 1 in the scale, stages with even numbers have high levels of oxygen-18 and represent cold glacial periods, while the odd-numbered stages are lows in the oxygen-18 figures, representing warm interglacial intervals. The data are derived from pollen and foraminifera (plankton) remains in drilled marine sediment cores, sapropels, and other data that reflect historic climate; these are called proxies. The MIS timescale was developed from the pioneering work of Cesare Emiliani in the 1950s, modifying an earlier system introduced by oceanographer Gustaf Arrhenius. It is now widely used in archaeology and other fields to express dating in the Quaternary period (the last 2.6 million years), as well as providing the fullest and best data for that period for paleoclimatology or the study of the early climate of the Earth, representing "the standard to which we correlate other Quaternary climate records". Emiliani's work in turn depended on Harold Urey's prediction in a paper of 1947 that the ratio between oxygen-18


id: marine-isotope-stages-20260505-1
file: data/en.wikipedia.org/wiki/Marine_isotope_stages-1.md
category: reference
title: Marine isotope stages
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Marine_isotope_stages
date_saved: 2026-05-05T09:55:34.928910+00:00
instance: kb-cron
parent: marine-isotope-stages-20260505-0
summary:
The following are the start dates (apart from MIS 5 sub-stages) of the most recent MIS (Lisiecki & Raymo 2005, LR04 Benthic Stack). The figures, in thousands of years ago, are from Lisiecki's website. Numbers for substages in MIS 5 denote peaks of substages rather than boundaries. MIS Start date MIS 1 – 14 kya, end of the Younger Dryas marks the start of the Holocene. The LR04 date of 14 kya had to accommodate less well studied time intervals, and the generally accepted date of 11.7 kya is to be preferred. MIS 2 – 29 (Last Glacial Maximum) MIS 3 – 57 (MIS 5d- MIS 2 is called the Last Glacial Period, Wisconsinan glaciation in North America, Weichselian glaciation in northern Europe) MIS 4 – 71 MIS 5 – 130, usually sub-divided into a to e: MIS 5a – 82 (peak of interglacial sub-stage) MIS 5b – 87 (peak of glacial sub-stage) MIS 5c – 96 (peak of interglacial sub-stage) MIS 5d – 109 (peak of glacial sub-stage) MIS 5e – 123 (peak of Last Interglacial, also known as the Eemian among other names) MIS 6 – 191 (Penultimate Glacial Period, also called Illinoian glacial in North America, later


id: miyake-event-20260505-0
file: data/en.wikipedia.org/wiki/Miyake_event-0.md
category: reference
title: Miyake event
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Miyake_event
date_saved: 2026-05-05T09:55:36.116294+00:00
instance: kb-cron
parent: miyake-event-20260505-0
summary:
A Miyake event is an observed sharp enhancement of the production of cosmogenic isotopes by cosmic rays. It can be marked by a spike in the concentration of radioactive carbon isotope 14C in tree rings, as well as 10Be and 36Cl in ice cores, which are all independently dated. At present, five significant events are known (7176 BCE, 5259 BCE, 664-663 BCE (historically referred to as 660 BCE), 774 CE, 993 CE) for which the spike in 14C is quite remarkable, i.e. above 1% rise over a period of two years, and four more events (12,350 BCE, 5410 BCE, 1052 CE, 1279 CE) need independent confirmation. It is not known how often Miyake events occur, but from the available data it is estimated to be every 400 to 2,400 years. A Miyake event occurring in modern conditions would cause severe damage to global technological infrastructure such as satellites, telecommunications, and power grids. There is strong evidence that Miyake events are caused by extreme solar particle events and they are likely related to super-flares discovered on solar-like stars. Although Miyake events are based on extreme year-to-year rises of 14C concentration, the duration of the periods over which the 14C levels increase


id: nucleocosmochronology-20260505-0
file: data/en.wikipedia.org/wiki/Nucleocosmochronology-0.md
category: reference
title: Nucleocosmochronology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nucleocosmochronology
date_saved: 2026-05-05T09:55:37.317730+00:00
instance: kb-cron
parent: nucleocosmochronology-20260505-0
summary:
Nucleocosmochronology, or nuclear cosmochronology, is a technique used to determine timescales for astrophysical objects and events based on observed ratios of radioactive heavy elements and their decay products. It is similar in many respects to radiometric dating, in which trace radioactive impurities were selectively incorporated into materials when they were formed. To calculate the age of formation of astronomical objects, the observed ratios of abundances of heavy radioactive and stable nuclides are compared to the primordial ratios predicted by nucleosynthesis theory. Both radioactive elements and their decay products matter, and some important elements include the long-lived radioactive nuclei Th-232, U-235, and U-238, all formed by the r-process. The process has been compared to radiocarbon dating. The age of the objects are determined by placing constraints on the duration of nucleosynthesis in the galaxy. Nucleocosmochronology has been employed to determine the age of the Sun (4.57±0.02 billion years) and of the Galactic thin disk (8.8±1.8 billion years), among other objects. It has also been used to estimate the age of the Milky Way itself by studying Cayrel's Star in the Galactic halo, which due to its low metallicity, is believed to have formed early in the history of the Galaxy. Limiting


id: optically-stimulated-luminescence-thermochronometry-20260505-0
file: data/en.wikipedia.org/wiki/Optically_stimulated_luminescence_thermochronometry-0.md
category: reference
title: Optically stimulated luminescence thermochronometry
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Optically_stimulated_luminescence_thermochronometry
date_saved: 2026-05-05T09:55:38.553390+00:00
instance: kb-cron
parent: optically-stimulated-luminescence-thermochronometry-20260505-0
summary:
Optically stimulated luminescence (OSL) thermochronometry is a dating method used to determine the time since quartz and/or feldspar began to store charge as it cools through the effective closure temperature. The closure temperature for quartz and Na-rich K-feldspar is 30-35 °C and 25 °C respectively. When quartz and feldspar are beneath the earth, they are hot. They cool when any geological process e.g. focused erosion causes their exhumation to the earth surface. As they cool, they trap electron charges originating from within the crystal lattice. These charges are accommodated within crystallographic defects or vacancies in their crystal lattices as the mineral cools below the closure temperature. During detrapping of these electrons, luminescence is produced. The luminescence or light emission from the mineral is assumed to be proportional to the trapped electron charge population. The age recorded in standard OSL method is determined by counting the number of these trapped charges in an OSL detection system. The OSL age is the cooling age of the quartz and/or feldspar. This cooling history is a record of the mineral's thermal history, which is used to reconstruct the geological event. The sub-Quaternary period (104 to 105 years) is the geological age where OSL is


id: optically-stimulated-luminescence-thermochronometry-20260505-1
file: data/en.wikipedia.org/wiki/Optically_stimulated_luminescence_thermochronometry-1.md
category: reference
title: Optically stimulated luminescence thermochronometry
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Optically_stimulated_luminescence_thermochronometry
date_saved: 2026-05-05T09:55:38.553390+00:00
instance: kb-cron
parent: optically-stimulated-luminescence-thermochronometry-20260505-0
summary:
== Sample preparation == Bedrock samples from earth surface or boreholes are required earth materials for OSL dating. Minerals (quartz and/or feldspar) are usually separated from the rock or sediment samples under regulated laboratory lighting system similar to procedures used in archaeological OSL dating. The light source is usually a controlled red light condition to avoid luminescence signal resetting.[7] Crushing of sample are gently carried out to avoid generating heat that is strong enough to reset OSL signal in the minerals. Crushed samples are separated by means of a sieve to get fine-grained. A range of values varying from 90 – 125 microns, 100 – 200 microns and 180 – 212 microns can be used for OSL measurement. The selected grains are chemically treated with HCl to digest carbonates and with H2O2 to remove organic materials that can contaminate the sensitivity of OSL signal during measurement. Feldspar and quartz with densities of < 2.62 g cm−3 and < 2.68 g cm−3 respectively are separated from other heavier minerals by density separation. Inclusions of zircon, apatite and feldspar in quartz as well as alpha-particles irradiated grain edges that can contaminate OSL signal are removed by etching in hydrofluoric acid (HF). ==


id: optically-stimulated-luminescence-thermochronometry-20260505-2
file: data/en.wikipedia.org/wiki/Optically_stimulated_luminescence_thermochronometry-2.md
category: reference
title: Optically stimulated luminescence thermochronometry
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Optically_stimulated_luminescence_thermochronometry
date_saved: 2026-05-05T09:55:38.553390+00:00
instance: kb-cron
parent: optically-stimulated-luminescence-thermochronometry-20260505-0
summary:
=== Equivalent dose determination === The equivalent dose ( D E {\displaystyle D_{E}} ) is also known as the dose response is determined from the dose response curve (see Plot B). The single-aliquot regenerative (SAR) protocol is a commonly used method for the determination of the equivalent dose. The protocol involves series of laboratory measurement of OSL signal (see Plot A), which is emitted by the aliquot after it has been optically stimulated at a known beta dose within a given time in seconds. The beta-source may be 90Sr/90Y in an automated Ris ∅ {\displaystyle \varnothing } Thermal Luminescence Reader . During SAR protocol, the difference in the measurement for quartz and feldspar is mainly on the degree of heat required per time and the source of stimulation. The first stage involves determination of the natural dose (see Plot B) preheating the aliquot to about 160 -130 °C (for feldspar) for 10 s or 160-300 °C (for quartz) when the natural luminescence signal (i.e. natural dose) is still intact. This is done to remove unstable signals in the mineral. After preheating, the aliquot is optically stimulated by Infrared light emitting diode (for feldspar) or Blue light emitting diode (for quartz)


id: orbital-tuning-20260505-0
file: data/en.wikipedia.org/wiki/Orbital_tuning-0.md
category: reference
title: Orbital tuning
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orbital_tuning
date_saved: 2026-05-05T09:55:39.748346+00:00
instance: kb-cron
parent: orbital-tuning-20260505-0
summary:
Orbital tuning refers to the process of adjusting the time scale of a paleoenvironmental proxy record so that the observed fluctuations correspond to the Milankovitch cycles in the Earth's orbital motion. This is typically done to correct for dating uncertainty or in cases where dating is not possible, such as beyond the range of radiocarbon dating. == Description == Changes in the Earth's orbit affect the amount and distribution of sunlight the Earth and certain parts of the Earth receives. Such changes are expected to introduce periodic climate changes on a time scale of 20-100 kyr. Long records of sedimentation or climate should record such variations. However, such records often have poorly constrained age scales. As a result, scientists will sometimes adjust the timing of the features in their samples to match the predictions of orbital theory in the hopes of improving the accuracy of their data. == Methods and uses == Orbital tuning is done by adjusting the timescale of a paleoclimate record to match variations in an insolated record. These small adjustments synchronize the paleoclimate proxy record to that of orbital cycles. If they are not, scientists are able to adjust one or more points to have these


id: principle-of-faunal-succession-20260505-0
file: data/en.wikipedia.org/wiki/Principle_of_faunal_succession-0.md
category: reference
title: Principle of faunal succession
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Principle_of_faunal_succession
date_saved: 2026-05-05T09:55:40.963821+00:00
instance: kb-cron
parent: principle-of-faunal-succession-20260505-0
summary:
The principle of faunal succession, also known as the law of faunal succession, is based on the observation that sedimentary rock strata contain fossilized flora and fauna, and that these fossils succeed each other vertically in a specific, reliable order that can be identified over wide horizontal distances. A fossilized Neanderthal bone (less than 500,000 years old) will never be found in the same stratum as a fossilized Megalosaurus (about 160 million years old), for example, because neanderthals and megalosaurs lived during different geological periods, separated by millions of years. This allows for strata to be identified and dated by the fossils found within. This principle, which received its name from the English geologist William Smith, is of great importance in determining the relative age of rocks and strata. The fossil content of rocks together with the law of superposition helps to determine the time sequence in which sedimentary rocks were laid down. Evolution explains the observed faunal and floral succession preserved in rocks. Faunal succession was documented by Smith in England during the first decade of the 19th century, and concurrently in France by Cuvier (with the assistance of the mineralogist Alexandre Brongniart). Archaic biological features and organisms are


id: relative-dating-20260505-0
file: data/en.wikipedia.org/wiki/Relative_dating-0.md
category: reference
title: Relative dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Relative_dating
date_saved: 2026-05-05T09:55:42.146774+00:00
instance: kb-cron
parent: relative-dating-20260505-0
summary:
Relative dating is the science of determining the relative order of past events (i.e., the age of an object in comparison to another), without necessarily determining their absolute age (i.e., estimated age). In geology, rock or superficial deposits, fossils and lithologies can be used to correlate one stratigraphic column with another. Prior to the discovery of radiometric dating in the early 20th century, which provided a means of absolute dating, archaeologists and geologists used relative dating to determine ages of materials. Though relative dating can only determine the sequential order in which a series of events occurred, not when they occurred, it remains a useful technique. Relative dating by biostratigraphy is the preferred method in paleontology and is, in some respects, more accurate. The Law of Superposition, which states that older layers will be deeper in a site than more recent layers, was the summary outcome of 'relative dating' as observed in geology from the 17th century to the early 20th century. == Geology == The regular order of the occurrence of fossils in rock layers was discovered around 1800 by William Smith. While digging the Somerset Coal Canal in Southwest England, he found that fossils were always in the


id: relative-dating-20260505-1
file: data/en.wikipedia.org/wiki/Relative_dating-1.md
category: reference
title: Relative dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Relative_dating
date_saved: 2026-05-05T09:55:42.146774+00:00
instance: kb-cron
parent: relative-dating-20260505-0
summary:
The principle of lateral continuity states that layers of sediment initially extend laterally in all directions; in other words, they are laterally continuous. As a result, rocks that are otherwise similar, but are now separated by a valley or other erosional feature, can be assumed to be originally continuous. Layers of sediment do not extend indefinitely; rather, the limits can be recognized and are controlled by the amount and type of sediment available and the size and shape of the sedimentary basin. Sediment will continue to be transported to an area and it will eventually be deposited. However, the layer of that material will become thinner as the amount of material lessens away from the source. Often, coarser-grained material can no longer be transported to an area because the transporting medium has insufficient energy to carry it to that location. In its place, the particles that settle from the transporting medium will be finer-grained, and there will be a lateral transition from coarser- to finer-grained material. The lateral variation in sediment within a stratum is known as sedimentary facies. If sufficient sedimentary material is available, it will be deposited up to the limits of the sedimentary basin. Often, the sedimentary


id: relative-dating-20260505-2
file: data/en.wikipedia.org/wiki/Relative_dating-2.md
category: reference
title: Relative dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Relative_dating
date_saved: 2026-05-05T09:55:42.146774+00:00
instance: kb-cron
parent: relative-dating-20260505-0
summary:
== Ecology == Dating the layers of bird nests made from plastic pollution can be done by checking the expiration date on food packaging. By doing so, a stratigraphy emerges, documenting the history of the breeding site and reflecting all earlier breeding attempts. A nest found in Amsterdam found in 2021 could be traced back all the way to the early 90s. The oldest nest item was a Mars bar advertising the 1994 FIFA World Cup. Single-use packages of perishable products like 'fresh milk' or a 'ripe avocado' proved to be very precise marker in time referring not only to the year but also to the near exact date it was most likely consumed. Non-food or nonperishable, shelf-stable products, could result in less precise dating. == Archaeology == Relative dating methods in archaeology are similar to some of those applied in geology. The principles of typology can be compared to the biostratigraphic approach in geology. == See also == Astronomical chronology Age of the Earth Age of the universe Chronological dating, archaeological chronology Absolute dating Relative dating, this article Phase (archaeology) Archaeological association Archaeological context Archaeological culture – Group of artifact types and structure layouts that often occur together Relationship


id: skeletochronology-20260505-0
file: data/en.wikipedia.org/wiki/Skeletochronology-0.md
category: reference
title: Skeletochronology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Skeletochronology
date_saved: 2026-05-05T09:55:43.322398+00:00
instance: kb-cron
parent: skeletochronology-20260505-0
summary:
Skeletochronology is a technique used to determine the individual, chronological ages of vertebrates by counting lines of arrested, annual growth, also known as LAGs, within skeletal tissues. Within the annual bone growth specimens, there are broad and narrow lines. Broad lines represent the growth period and narrow lines represent a growth pause. These narrow lines are what characterises one growth year, therefore make it suitable to determine the age of the specimen. Not all bones grow at the same rate and the individual growth rate of a bone changes over a lifetime, therefore periodic growth marks can take irregular patterns. This indicates significant chronological events in an individual's life. The use of bone as a biomaterial is useful in investigating structure-property relationships. In addition to current research in skeletochronology, the ability of bone to adapt and change its structure to the external environment provides potential for further research in bone histomorphometry in the future. Amphibians and Reptiles are commonly aged determined, using this method, because they undergo discrete annual activity cycles such as winter dormancy or metamorphosis, however it cannot be used for all species of bony animals. The different environmental and biological factors that influence bone growth and development


id: tempore-20260505-0
file: data/en.wikipedia.org/wiki/Tempore-0.md
category: reference
title: Tempore
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Tempore
date_saved: 2026-05-05T09:55:44.514312+00:00
instance: kb-cron
parent: tempore-20260505-0
summary:
Tempore (abbreviated to temp.) in historical literature, denotes a period during which a person whose exact lifespan is unknown, was known to have been alive or active, or some other date which is not exactly known, usually given as the reign of a monarch. The word is Latin, being the ablative singular of the noun tempus, temporis, "time", thus meaning "in the time (of)". It should be followed by a name in the genitive case. The theoretical full form might be vixit tempore Regis Henrici Primi ("they lived in the time of King Henry the First"; i.e. 1100–1135). The best-known occurrence is in the Domesday Book of 1086, where the phrase Tempore Regis Eduardi (nominative case Rex Eduardus), meaning "in the time of King Edward (the Confessor)" appears in the entry for almost every manor, abbreviated as TRE. It thus signifies the date range 1042–1066. It is useful in historical literature because the names of many historical persons appear in surviving documents only in royal charters, possibly as witnesses, which can be dated to the reign of the originating monarch. The word tempore is often given in its abbreviated form temp. It is similar to floruit ("flourished" [at a date


id: terminus-post-quem-20260505-0
file: data/en.wikipedia.org/wiki/Terminus_post_quem-0.md
category: reference
title: Terminus post quem
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Terminus_post_quem
date_saved: 2026-05-05T09:55:45.704663+00:00
instance: kb-cron
parent: terminus-post-quem-20260505-0
summary:
A terminus post quem ('limit after which', sometimes abbreviated TPQ) and terminus ante quem ('limit before which', abbreviated TAQ) specify the known limits of dating for events or items. A terminus post quem is the earliest date the event may have happened or the item was in existence, and a terminus ante quem is the latest. An event may well have both a terminus post quem and a terminus ante quem, in which case the limits of the possible range of dates are known at both ends, but many events have just one or the other. Similarly, a terminus ad quem 'limit to which' is the latest possible date of a non-punctual event (period, era, etc.), whereas a terminus a quo 'limit from which' is the earliest. The concepts are similar to those of upper and lower bounds in mathematics. These terms are often used in archaeological and historical studies, such as dating layers in excavated sites, coins, historical events, authors, inscriptions or texts where the exact dates may not be known or may be in dispute. == Example == For example, consider an archaeological find of a burial that contains coins dating to 1588, 1595, and others less securely


id: thermoluminescence-dating-20260505-0
file: data/en.wikipedia.org/wiki/Thermoluminescence_dating-0.md
category: reference
title: Thermoluminescence dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Thermoluminescence_dating
date_saved: 2026-05-05T09:55:46.871512+00:00
instance: kb-cron
parent: thermoluminescence-dating-20260505-0
summary:
Thermoluminescence dating (TL) is the determination, by means of measuring the accumulated radiation dose, of the time elapsed since material containing crystalline minerals was either heated (lava, ceramics) or exposed to sunlight (sediments). As a crystalline material is heated during measurements, the process of thermoluminescence starts. Thermoluminescence emits a weak light signal that is proportional to the radiation dose absorbed by the material. It is a type of luminescence dating. The technique has wide application, and is relatively cheap at some US$300–700 per object; ideally a number of samples are tested. Sediments are more expensive to date. The destruction of a relatively significant amount of sample material is necessary, which can be a limitation in the case of artworks. The heating must have taken the object above 500 °C, which covers most ceramics, although very high-fired porcelain creates other difficulties. It will often work well with stones that have been heated by fire. The clay core of bronze sculptures made by lost wax casting is also able to be tested. Different materials vary considerably in their suitability for the technique, depending on several factors. Subsequent irradiation, for example if an x-ray is taken, can affect accuracy, as will the "annual


id: thermoluminescence-dating-20260505-1
file: data/en.wikipedia.org/wiki/Thermoluminescence_dating-1.md
category: reference
title: Thermoluminescence dating
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Thermoluminescence_dating
date_saved: 2026-05-05T09:55:46.871512+00:00
instance: kb-cron
parent: thermoluminescence-dating-20260505-0
summary:
== See also == Geochronology Luminescence dating Rehydroxylation dating Thermoluminescent dosimeter == Notes == Oxford Authentication: Home - TL Testing Authentication 'Oxford Authentication® Ltd authenticates ceramic antiquities using the scientific technique of thermoluminescence (TL). TL testing is a dating method for archaeological items which can distinguish between genuine and fake antiquities.' See some of their case studies here: https://www.oxfordauthentication.com/case-studies/ == References and bibliography == GlobalNet.co.uk, Quaternary TL Surveys - Guide to thermoluminescence date measurement Aitken, M.J., Thermoluminescence Dating, Academic Press, London (1985) – Standard text for introduction to the field. Quite complete and rather technical, but well written and well organized. There is a second edition. Aitken, M.J., Introduction to Optical Dating, Oxford University Press (1998) – Good introduction to the field. Keizars, K.Z. 2003. NRTL as a method of analysis of sand transport along the coast of the St. Joseph Peninsula, Florida. GAC/MAC 2003. Presentation: Brock University, St. Catharines, Ontario, Canada. JCRonline.org, Ķeizars, Z., Forrest, B., Rink, W.J. 2008. Natural Residual Thermoluminescence as a Method of Analysis of Sand Transport along the Coast of the St. Joseph Peninsula, Florida. Journal of Coastal Research, 24: 500–507. Keizars, Z. 2008b. NRTL trends observed in the sands of St. Joseph Peninsula, Florida.


id: evidence-20260505-0
file: data/en.wikipedia.org/wiki/Evidence-0.md
category: reference
title: Evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence
date_saved: 2026-05-05T09:55:48.385077+00:00
instance: kb-cron
parent: evidence-20260505-0
summary:
Evidence for a proposition is what supports the proposition. It is usually understood as an indication that the proposition is true. The exact definition and role of evidence vary across different fields. In epistemology, evidence is what justifies beliefs or what makes it rational to hold a certain doxastic attitude. For example, a perceptual experience of a tree may serve as evidence to justify the belief that there is a tree. In this role, evidence is usually understood as a private mental state. In phenomenology, evidence is limited to intuitive knowledge, often associated with the controversial assumption that it provides indubitable access to truth. In science, scientific evidence is information gained through the scientific method that confirms or disconfirms scientific hypotheses, acting as a neutral arbiter between competing theories. Measurements of Mercury's "anomalous" orbit, for example, are seen as evidence that confirms Einstein's theory of general relativity. The problems of underdetermination and theory-ladenness are two obstacles that threaten to undermine the role of scientific evidence. Philosophers of science tend to understand evidence not as mental states but as verifiable information, observable physical objects or events, secured by following the scientific method. In law, evidence is information to establish or refute


id: evidence-20260505-1
file: data/en.wikipedia.org/wiki/Evidence-1.md
category: reference
title: Evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence
date_saved: 2026-05-05T09:55:48.385077+00:00
instance: kb-cron
parent: evidence-20260505-0
summary:
The evident instills certainty and grants the knower a subjective sense of security, as they believe to have aligned with the truth Initially, evident truths are perceived as natural and effortless, as Aristotle highlighted. They are innately present within the intellect, fostering a peaceful and harmonious understanding. Consequently, evident truths appear to be widely shared, strongly connected to common sense, which comprises generally accepted beliefs. Evident truths are fertile ground: they provide a solid foundation for other branches of scientific knowledge to flourish. These ten characteristics of what is evident allowed Riofrio to formulate a test of evidence to detect the level of certainty or evidence that one argument or proof could have. === Evidential relation === Philosophers in the 20th century started to investigate the "evidential relation", the relation between evidence and the proposition supported by it. The issue of the nature of the evidential relation concerns the question of what this relation has to be like in order for one thing to justify a belief or to confirm a hypothesis. Important theories in this field include the probabilistic approach, hypothetico-deductivism and the positive-instance approach. Probabilistic approaches, also referred to as Bayesian confirmation theory, explain the evidential relation in


id: evidence-20260505-2
file: data/en.wikipedia.org/wiki/Evidence-2.md
category: reference
title: Evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence
date_saved: 2026-05-05T09:55:48.385077+00:00
instance: kb-cron
parent: evidence-20260505-0
summary:
==== In epistemology ==== The guiding intuition within epistemology concerning the role of evidence is that it is what justifies beliefs. For example, Phoebe's auditory experience of the music justifies her belief that the speakers are on. Evidence has to be possessed by the believer in order to play this role. So Phoebe's own experiences can justify her own beliefs but not someone else's beliefs. Some philosophers hold that evidence possession is restricted to conscious mental states, for example, to sense data. This view has the implausible consequence that many of simple everyday-beliefs would be unjustified. The more common view is that all kinds of mental states, including stored beliefs that are currently unconscious, can act as evidence. It is sometimes argued that the possession of a mental state capable of justifying another is not sufficient for the justification to happen. The idea behind this line of thought is that justified belief has to be connected to or grounded in the mental state acting as its evidence. So Phoebe's belief that the speakers are on is not justified by her auditory experience if the belief is not based in this experience. This would be the case, for example, if Phoebe


id: evidence-20260505-3
file: data/en.wikipedia.org/wiki/Evidence-3.md
category: reference
title: Evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence
date_saved: 2026-05-05T09:55:48.385077+00:00
instance: kb-cron
parent: evidence-20260505-0
summary:
==== In philosophy of science ==== In the sciences, evidence is understood as what confirms or disconfirms scientific hypotheses. The term "confirmation" is sometimes used synonymously with that of "evidential support". Measurements of Mercury's "anomalous" orbit, for example, are seen as evidence that confirms Einstein's theory of general relativity. This is especially relevant for choosing between competing theories. So in the case above, evidence plays the role of neutral arbiter between Newton's and Einstein's theory of gravitation. This is only possible if scientific evidence is public and uncontroversial so that proponents of competing scientific theories agree on what evidence is available. These requirements suggest scientific evidence consists not of private mental states but of public physical objects or events. It is often held that evidence is in some sense prior to the hypotheses it confirms. This was sometimes understood as temporal priority, i.e. that we come first to possess the evidence and later form the hypothesis through induction. But this temporal order is not always reflected in scientific practice, where experimental researchers may look for a specific piece of evidence in order to confirm or disconfirm a pre-existing hypothesis. Logical positivists, on the other hand, held that this priority is


id: evidence-20260505-4
file: data/en.wikipedia.org/wiki/Evidence-4.md
category: reference
title: Evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence
date_saved: 2026-05-05T09:55:48.385077+00:00
instance: kb-cron
parent: evidence-20260505-0
summary:
In law, the production and presentation of evidence depend first on establishing on whom the burden of proof lies. Admissible evidence is that which a court receives and considers for the purposes of deciding a particular case. Two primary burden-of-proof considerations exist in law. The first is on whom the burden rests. In many, especially Western, courts, the burden of proof is placed on the prosecution in criminal cases and the plaintiff in civil cases. The second consideration is the degree of certitude proof must reach, depending on both the quantity and quality of evidence. These degrees are different for criminal and civil cases, the former requiring evidence beyond a reasonable doubt, the latter considering only which side has the preponderance of evidence, or whether the proposition is more likely true or false. The parts of a legal case that are not in controversy are known, in general, as the "facts of the case." Beyond any facts that are undisputed, a judge or jury is usually tasked with being a trier of fact for the other issues of a case. Evidence and rules are used to decide questions of fact that are disputed, some of which may be determined by


id: empirical-evidence-20260505-0
file: data/en.wikipedia.org/wiki/Empirical_evidence-0.md
category: reference
title: Empirical evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence
date_saved: 2026-05-05T09:55:49.542435+00:00
instance: kb-cron
parent: empirical-evidence-20260505-0
summary:
Empirical evidence is evidence obtained through sense experience or experimental procedure. It is of central importance to the sciences and plays a role in various other fields, like epistemology and law. There is no general agreement on how the terms evidence and empirical are to be defined. Often different fields work with quite different conceptions. In epistemology, evidence is what justifies beliefs or what determines whether holding a certain belief is rational. This is only possible if the evidence is possessed by the person, which has prompted various epistemologists to conceive evidence as private mental states like experiences or other beliefs. In philosophy of science, on the other hand, evidence is understood as that which confirms or disconfirms scientific hypotheses and arbitrates between competing theories. For this role, evidence must be public and uncontroversial, like observable physical objects or events and unlike private mental states, so that evidence may foster scientific consensus. The term empirical comes from Greek ἐμπειρία empeiría, i.e. 'experience'. In this context, it is usually understood as what is observable, in contrast to unobservable or theoretical objects. It is generally accepted that unaided perception constitutes observation, but it is disputed to what extent objects accessible only to


id: empirical-evidence-20260505-1
file: data/en.wikipedia.org/wiki/Empirical_evidence-1.md
category: reference
title: Empirical evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence
date_saved: 2026-05-05T09:55:49.542435+00:00
instance: kb-cron
parent: empirical-evidence-20260505-0
summary:
== Definition == A thing is evidence for a proposition if it epistemically supports this proposition or indicates that the supported proposition is true. Evidence is empirical if it is constituted by or accessible to sensory experience. There are various competing theories about the exact definition of the terms evidence and empirical. Different fields, like epistemology, the sciences or legal systems, often associate different concepts with these terms. An important distinction among theories of evidence is whether they identify evidence with private mental states or with public physical objects. Concerning the term empirical, there is a dispute about where to draw the line between observable or empirical objects in contrast to unobservable or merely theoretical objects. The traditional view proposes that evidence is empirical if it is constituted by or accessible to sensory experience. This involves experiences arising from the stimulation of the sense organs, like visual or auditory experiences, but the term is often used in a wider sense including memories and introspection. It is usually seen as excluding purely intellectual experiences, like rational insights or intuitions used to justify basic logical or mathematical principles. The terms empirical and observable are closely related and sometimes used as synonyms. There


id: empirical-evidence-20260505-2
file: data/en.wikipedia.org/wiki/Empirical_evidence-2.md
category: reference
title: Empirical evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence
date_saved: 2026-05-05T09:55:49.542435+00:00
instance: kb-cron
parent: empirical-evidence-20260505-0
summary:
=== Empiricism and rationalism === In its strictest sense, empiricism is the view that all knowledge is based on experience or that all epistemic justification arises from empirical evidence. This stands in contrast to the rationalist view, which holds that some knowledge is independent of experience, either because it is innate or because it is justified by reason or rational reflection alone. Expressed through the distinction between knowledge a priori and a posteriori from the previous section, rationalism affirms that there is knowledge a priori, which is denied by empiricism in this strict form. One difficulty for empiricists is to account for the justification of knowledge pertaining to fields like mathematics and logic, for example, that 3 is a prime number or that modus ponens is a valid form of deduction. The difficulty is due to the fact that there seems to be no good candidate of empirical evidence that could justify these beliefs. Such cases have prompted empiricists to allow for certain forms of knowledge a priori, for example, concerning tautologies or relations between our concepts. These concessions preserve the spirit of empiricism insofar as the restriction to experience still applies to knowledge about the external world. In some


id: anecdotal-evidence-20260505-0
file: data/en.wikipedia.org/wiki/Anecdotal_evidence-0.md
category: reference
title: Anecdotal evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anecdotal_evidence
date_saved: 2026-05-05T09:55:50.761018+00:00
instance: kb-cron
parent: anecdotal-evidence-20260505-0
summary:
Anecdotal evidence (or anecdata) is evidence based on descriptions and reports of individual, personal experiences, or observations, collected in a non-systematic manner. The term anecdotal encompasses a variety of forms of evidence, including personal experiences, self-reported claims, eyewitness accounts of others, and those from fictional sources, making it a broad category that can lead to confusion due to its varied interpretations. Anecdotal evidence can be true or false but is not usually subjected to scholarly methods, scientific methods, or rules of legal, historical, academic, or intellectual rigor, meaning there are little or no safeguards against fabrication or inaccuracy. However, the use of anecdotal reports in advertising or promotion of a product, service, or idea may be considered a testimonial, which is highly regulated in certain jurisdictions. The persuasiveness of anecdotal evidence compared to that of statistical evidence has been a subject of debate; some studies have argued that there is a generalized tendency to overvalue anecdotal evidence, whereas others argue the contrary. == Scientific context == In science, definitions of anecdotal evidence include: "casual observations/indications rather than rigorous or scientific analysis" "information passed along by word-of-mouth but not documented scientifically" "evidence that comes from an individual experience. This may be


id: attestation-20260505-0
file: data/en.wikipedia.org/wiki/Attestation-0.md
category: reference
title: Attestation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Attestation
date_saved: 2026-05-05T09:55:53.102961+00:00
instance: kb-cron
parent: attestation-20260505-0
summary:
An attestation is something that serves to bear witness, confirm, authenticate or verify the validity of some fact or status. An attestor is someone who performs an attestation. An attestation date is the date on which an attestation is performed. == Examples == Examples of attestations include: Testimony, a sworn verification of the truth of a set of factual statements An attestation clause, verifying a document A police oath or an oath of allegiance in armed forces of the United Kingdom, pledging loyalty or the faithful execution of duties A validation of the integrity of a computing device such as a server needed for trusted computing == See also == The dictionary definition of attest at Wiktionary The dictionary definition of attestation at Wiktionary The dictionary definition of attestor at Wiktionary Attested language, a language for which documented evidence exists == References ==


id: burden-of-proof-philosophy-20260505-0
file: data/en.wikipedia.org/wiki/Burden_of_proof_(philosophy)-0.md
category: reference
title: Burden of proof (philosophy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Burden_of_proof_(philosophy)
date_saved: 2026-05-05T09:55:54.272353+00:00
instance: kb-cron
parent: burden-of-proof-philosophy-20260505-0
summary:
The burden of proof (Latin: onus probandi, shortened from Onus probandi incumbit ei qui dicit, non ei qui negat – the burden of proof lies with the one who speaks, not the one who denies) is the obligation on a party in a dispute to provide sufficient warrant for its position. == Holder of the burden == When two parties are in a discussion and one makes a claim that the other disputes, the one who makes the claim typically has a burden of proof to justify or substantiate that claim, especially when it challenges a perceived status quo. This is also stated in Hitchens's razor, which declares that "what may be asserted without evidence may be dismissed without evidence." Carl Sagan proposed a related criterion: "Extraordinary claims require extraordinary evidence". While certain kinds of arguments, such as logical syllogisms, require mathematical or strictly logical proofs, the standard for evidence to meet the burden of proof is usually determined by context and community standards and conventions. Philosophical debate can devolve into arguing about who has the burden of proof about a particular claim. This has been described as "burden tennis" or the "onus game". == Shifting the burden of proof


id: burden-of-proof-philosophy-20260505-1
file: data/en.wikipedia.org/wiki/Burden_of_proof_(philosophy)-1.md
category: reference
title: Burden of proof (philosophy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Burden_of_proof_(philosophy)
date_saved: 2026-05-05T09:55:54.272353+00:00
instance: kb-cron
parent: burden-of-proof-philosophy-20260505-0
summary:
In inferential statistics, the null hypothesis is a general statement or default position that there is no relationship between two measured phenomena, or no association among groups. Rejecting or disproving the null hypothesis—and thus concluding that there are grounds for believing that there is a relationship between two phenomena (e.g. that a potential treatment has a measurable effect)—is a central task in the modern practice of science; the field of statistics gives precise criteria for rejecting a null hypothesis. The null hypothesis is generally assumed to be true until evidence indicates otherwise. In statistics, it is often denoted H0 (read "H-nought", "H-null", "H-oh", or "H-zero"). The concept of a null hypothesis is used differently in two approaches to statistical inference. In the significance testing approach of Ronald Fisher, a null hypothesis is rejected if the observed data are significantly unlikely to have occurred if the null hypothesis were true. In this case the null hypothesis is rejected and an alternative hypothesis is accepted in its place. If the data are consistent with the null hypothesis, then the null hypothesis is not rejected. In neither case is the null hypothesis or its alternative proven; the null hypothesis is tested with data


id: case-study-20260505-0
file: data/en.wikipedia.org/wiki/Case_study-0.md
category: reference
title: Case study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Case_study
date_saved: 2026-05-05T09:55:55.504712+00:00
instance: kb-cron
parent: case-study-20260505-0
summary:
A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like world war, or more often the policy analysis of real-world problems affecting multiple stakeholders. Generally, a case study can highlight nearly any individual, group, organization, event, belief system, or action. A case study does not necessarily have to be one observation (N=1), but may include many observations (one or multiple individuals and entities across multiple time periods, all within the same case study). Research projects involving numerous cases are frequently called cross-case research, whereas a study of a single case is called within-case research. Case study research has been extensively practiced in both the social and natural sciences. == Definition == There are multiple definitions of case studies, which may emphasize the number of observations (a small N), the method (qualitative), the thickness of the research (a comprehensive


id: case-study-20260505-1
file: data/en.wikipedia.org/wiki/Case_study-1.md
category: reference
title: Case study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Case_study
date_saved: 2026-05-05T09:55:55.504712+00:00
instance: kb-cron
parent: case-study-20260505-0
summary:
Typical cases are cases that exemplify a stable cross-case relationship. These cases are representative of the larger population of cases, and the purpose of the study is to look within the case rather than compare it with other cases. Diverse cases are cases that have variations on the relevant X and Y variables. Due to the range of variation on the relevant variables, these cases are representative of the full population of cases. Extreme cases are cases that have an extreme value on the X or Y variable relative to other cases. Deviant cases are cases that defy existing theories and common sense. They not only have extreme values on X or Y (like extreme cases) but defy existing knowledge about causal relations. Influential cases are cases that are central to a model or theory (for example, Nazi Germany in theories of fascism and the far-right). Most similar cases are cases that are similar on all the independent variables, except the one of interest to the researcher. Most different cases are cases that are different on all the independent variables, except the one of interest to the researcher. For theoretical discovery, Jason Seawright recommends using deviant cases or extreme cases


id: case-study-20260505-2
file: data/en.wikipedia.org/wiki/Case_study-2.md
category: reference
title: Case study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Case_study
date_saved: 2026-05-05T09:55:55.504712+00:00
instance: kb-cron
parent: case-study-20260505-0
summary:
== Limitations == Designing Social Inquiry (also called "KKV"), an influential 1994 book written by Gary King, Robert Keohane, and Sidney Verba, primarily applies lessons from regression-oriented analysis to qualitative research, arguing that the same logics of causal inference can be used in both types of research. The authors' recommendation is to increase the number of observations (a recommendation that Barbara Geddes also makes in Paradigms and Sand Castles), because few observations make it harder to estimate multiple causal effects, as well as increase the risk that there is measurement error, and that an event in a single case was caused by random error or unobservable factors. KKV sees process-tracing and qualitative research as being "unable to yield strong causal inference" because qualitative scholars would struggle with determining which of many intervening variables truly links the independent variable with a dependent variable. The primary problem is that qualitative research lacks a sufficient number of observations to properly estimate the effects of an independent variable. They write that the number of observations could be increased through various means, but that would simultaneously lead to another problem: that the number of variables would increase and thus reduce degrees of freedom. Christopher H.


id: cherry-picking-20260505-0
file: data/en.wikipedia.org/wiki/Cherry_picking-0.md
category: reference
title: Cherry picking
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cherry_picking
date_saved: 2026-05-05T09:55:56.680790+00:00
instance: kb-cron
parent: cherry-picking-20260505-0
summary:
Cherry picking, suppressing evidence, or the fallacy of incomplete evidence is the act of pointing to individual cases or data that seem to confirm a particular position while ignoring a significant portion of related and similar cases or data that may contradict that position. Cherry picking may be committed intentionally or unintentionally. == Name == The term is based on the perceived process of harvesting fruit, such as cherries. The picker would be expected to select only the ripest and healthiest fruits. An observer who sees only the selected fruit may thus wrongly conclude that most, or even all, of the tree's fruit is in a likewise good condition. This can also give a false impression of the quality of the fruit (since it is only a sample and is not a representative sample). A concept sometimes confused with cherry picking is the idea of gathering only the fruit that is easy to harvest, while ignoring other fruit that is higher up on the tree and thus more difficult to obtain (see low-hanging fruit). Cherry picking has a negative connotation as the practice neglects, overlooks or directly suppresses evidence that could lead to a complete picture. Cherry picking can be


id: cherry-picking-20260505-1
file: data/en.wikipedia.org/wiki/Cherry_picking-1.md
category: reference
title: Cherry picking
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cherry_picking
date_saved: 2026-05-05T09:55:56.680790+00:00
instance: kb-cron
parent: cherry-picking-20260505-0
summary:
[…] You might think that one-sidedness is actually desirable when your goal is winning rather than discovering a complex and nuanced truth. If this is true, then it's true of every fallacy. If winning is persuading a decision-maker, then any kind of manipulation or deception that actually works is desirable. But in fact, while winning may sometimes be served by one-sidedness, it is usually better served by two-sidedness. If your argument (say) in court is one-sided, then you are likely to be surprised by a strong counter-argument for which you are unprepared. The lesson is to cultivate two-sidedness in your thinking about any issue. Beware of any job that requires you to truncate your own understanding. Card stacking is a propaganda technique that seeks to manipulate audience perception of an issue by emphasizing one side and repressing another. Such emphasis may be achieved through media bias or the use of one-sided testimonials, or by simply censoring the voices of critics. The technique is commonly used in speeches by political candidates to discredit their opponents and to make themselves seem more worthy. The term originates from the magician's gimmick of "stacking the deck", which involves presenting a deck of cards that


id: consilience-20260505-0
file: data/en.wikipedia.org/wiki/Consilience-0.md
category: reference
title: Consilience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Consilience
date_saved: 2026-05-05T09:55:57.901867+00:00
instance: kb-cron
parent: consilience-20260505-0
summary:
In science and history, consilience (also convergence of evidence or concordance of evidence) is the principle that evidence from independent, unrelated sources can "converge" on strong conclusions. That is, when multiple sources of evidence are in agreement, the conclusion can be very strong even when none of the individual sources of evidence is significantly so on its own. Most established scientific knowledge is supported by a convergence of evidence: if not, the evidence is comparatively weak, and there will probably not be a strong scientific consensus. The principle is based on unity of knowledge; measuring the same result by several different methods should lead to the same answer. For example, it should not matter whether one measures distances within the Giza pyramid complex by laser rangefinding, by satellite imaging, or with a metre-stick – in all three cases, the answer should be approximately the same. For the same reason, different dating methods in geochronology should concur, a result in chemistry should not contradict a result in geology, etc. The word consilience was originally coined as the phrase "consilience of inductions" by William Whewell (consilience refers to a "jumping together" of knowledge). The word comes from Latin com- "together" and -siliens


id: consilience-20260505-1
file: data/en.wikipedia.org/wiki/Consilience-1.md
category: reference
title: Consilience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Consilience
date_saved: 2026-05-05T09:55:57.901867+00:00
instance: kb-cron
parent: consilience-20260505-0
summary:
== Deviations == Consilience does not forbid deviations: in fact, since not all experiments are perfect, some deviations from established knowledge are expected. However, when the convergence is strong enough, then new evidence inconsistent with the previous conclusion is not usually enough to outweigh that convergence. Without an equally strong convergence on the new result, the weight of evidence will still favor the established result. This means that the new evidence is most likely to be wrong. Science denialism (for example, AIDS denialism) is often based on a misunderstanding of this property of consilience. A denier may promote small gaps not yet accounted for by the consilient evidence, or small amounts of evidence contradicting a conclusion without accounting for the pre-existing strength resulting from consilience. More generally, to insist that all evidence converge precisely with no deviations would be naïve falsificationism, equivalent to considering a single contrary result to falsify a theory when another explanation, such as equipment malfunction or misinterpretation of results, is much more likely. == In history == Historical evidence also converges in an analogous way. For example: if five ancient historians, none of whom knew each other, all claim that Julius Caesar seized power in Rome


id: empirical-evidence-for-the-spherical-shape-of-earth-20260505-0
file: data/en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth-0.md
category: reference
title: Empirical evidence for the spherical shape of Earth
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth
date_saved: 2026-05-05T09:55:59.111630+00:00
instance: kb-cron
parent: empirical-evidence-for-the-spherical-shape-of-earth-20260505-0
summary:
The roughly spherical shape of Earth can be empirically evidenced by many different types of observation, ranging from ground level, flight, or orbit. The spherical shape causes a number of effects and phenomena that when combined disprove flat Earth beliefs. These include the visibility of distant objects on Earth's surface; lunar eclipses; appearance of the Moon; observation of the sky from a certain altitude; observation of certain fixed stars from different locations; observing the Sun; surface navigation; grid distortion on a spherical surface; weather systems; gravity; and modern technology. == Visibility of distant objects on Earth's surface == On a completely flat Earth without obstructions (mountains, hills, valleys or volcanos), the ground itself would never obscure distant objects. A spherical surface has a horizon which is closer when viewed from a lower altitude. In theory, a person standing on the surface with eyes 1.8 metres (5 ft 11 in) above the ground can see the ground up to about 4.79 kilometres (2.98 mi) away, but a person at the top of the Eiffel Tower at 273 metres (896 ft) can see the ground up to about 58.98 kilometres (36.65 mi) away. This phenomenon permits a way of confirming that Earth's


id: empirical-evidence-for-the-spherical-shape-of-earth-20260505-1
file: data/en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth-1.md
category: reference
title: Empirical evidence for the spherical shape of Earth
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth
date_saved: 2026-05-05T09:55:59.111630+00:00
instance: kb-cron
parent: empirical-evidence-for-the-spherical-shape-of-earth-20260505-0
summary:
== Observation of the sky from altitude with the aid of a diagram == On a perfectly spherical Earth, not considering obstructions and atmospheric refraction, its surface blocks almost half the sky for an observer close against the surface (see horizon). Moving away from the surface of Earth means that the ground blocks less and less of the sky. For example, when viewed from the Moon, Earth blocks only a small portion of the sky because it is so distant. This effect of geometry means that, when viewed from a high mountain, flat ground or ocean blocks less than a hemisphere of the sky. With the presumption of a spherical Earth, an expedition commissioned by caliph al-Ma'mun used this fact to calculate Earth's circumference to within 7,920 kilometres (4,920 mi) of the correct value of around 40,000 kilometres (25,000 mi), and possibly as accurately as 180 kilometres (110 mi). The rate of change in the angle blocked by Earth as altitude increases would be different for a disk than for a sphere. The amount of surface blocked would be different for a mountain close to the edge of a flat Earth compared to a mountain in the middle of a


id: empirical-evidence-for-the-spherical-shape-of-earth-20260505-2
file: data/en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth-2.md
category: reference
title: Empirical evidence for the spherical shape of Earth
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth
date_saved: 2026-05-05T09:55:59.111630+00:00
instance: kb-cron
parent: empirical-evidence-for-the-spherical-shape-of-earth-20260505-0
summary:
== Observing the Sun == On a flat Earth, a Sun that shines in all directions would illuminate the entire surface at the same time, and all places would experience sunrise and sunset at the horizon at about the same time. With a spherical Earth, half the planet is in daylight at any given time and the other half experiences nighttime. When a given location on the spherical Earth is in sunlight, its antipode – the location exactly on the opposite side of Earth – is in darkness. The spherical shape of Earth causes the Sun to rise and set at different times in different places, and different locations get different amounts of sunlight each day. In order to explain day and night, time zones, and the seasons, some flat Earth theorists propose that the Sun does not emit light in all directions, but acts more like a spotlight, only illuminating part of the flat Earth at a time. This conjecture is not consistent with observation: At sunrise and sunset, a spotlight Sun would be up in the sky at least a little bit, rather than at the horizon where it is always actually observed. A spotlight Sun would also


id: empirical-evidence-for-the-spherical-shape-of-earth-20260505-3
file: data/en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth-3.md
category: reference
title: Empirical evidence for the spherical shape of Earth
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth
date_saved: 2026-05-05T09:55:59.111630+00:00
instance: kb-cron
parent: empirical-evidence-for-the-spherical-shape-of-earth-20260505-0
summary:
=== Observing sunlight before or after seeing Sun === It is possible to see sun-lit windows of nearby high-rise buildings from ground level a few minutes before seeing the sun rise or after seeing the sun set. On a non-curved, flat landmass it would only take seconds, due to minuscule ratio (compare ~45 meters / 150 feet of a 14-story building to intercontinental distances). If such a phenomenon were caused by a prismatic property of atmosphere in a flat world, with a relatively small source of light revolving around Earth (as in later, 1800's-dated, maps of Flat Earth), it would contradict with one's ability to see a proper panorama of starry sky at a time at night, rather than a small yet distorted, "stretched" patch of it. Likewise, the top of a mountain is illuminated before sunrise and after sunset, as are clouds. === Watching the sun set twice === On level ground, the difference in the distance to the horizon between lying down and standing up is large enough to watch the Sun set twice by quickly standing up immediately after seeing it set for the first time while lying down. This also can be done with an aerial


id: empirical-evidence-for-the-spherical-shape-of-earth-20260505-4
file: data/en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth-4.md
category: reference
title: Empirical evidence for the spherical shape of Earth
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth
date_saved: 2026-05-05T09:55:59.111630+00:00
instance: kb-cron
parent: empirical-evidence-for-the-spherical-shape-of-earth-20260505-0
summary:
=== Angle to the Sun at different locations === On a given day, if many different cities measure the angle of the Sun at local noon, the resulting data, when combined with the known distances between cities, shows that Earth has 180 degrees of north-south curvature. (A full range of angles will be observed if the north and south poles are included, and the day chosen is either the autumnal or spring equinox.) This is consistent with many rounded shapes, including a sphere, and is inconsistent with a flat shape. Some claim that this experiment assumes a very distant Sun, such that the incoming rays are essentially parallel, and if a flat Earth is assumed, that the measured angles can allow one to calculate the distance to the Sun, which must be small enough that its incoming rays are not very parallel. However, if more than two relatively well-separated cities are included in the experiment, the calculation will make clear whether the Sun is distant or nearby. For example, on the equinox, the 0-degree angle from the North Pole and the 90-degree angle from the equator predict a Sun which would have to be located essentially next to the surface


id: empirical-evidence-for-the-spherical-shape-of-earth-20260505-5
file: data/en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth-5.md
category: reference
title: Empirical evidence for the spherical shape of Earth
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth
date_saved: 2026-05-05T09:55:59.111630+00:00
instance: kb-cron
parent: empirical-evidence-for-the-spherical-shape-of-earth-20260505-0
summary:
Because Earth is spherical, long-distance travel sometimes requires heading in different directions than one would head on a flat Earth. An example would be an airplane travelling 10,000 kilometres (6,200 mi) in a straight line, taking a 90-degree right turn, travelling another 10,000 kilometres (6,200 mi), taking another 90-degree right turn, and travelling 10,000 kilometres (6,200 mi) a third time. On a flat Earth, the aircraft would have travelled along three sides of a square, and arrive at a spot about 10,000 kilometres (6,200 mi) from where it started. But because Earth is spherical, in reality it will have travelled along three sides of a triangle, and arrive back very close to its starting point. If the starting point is the North Pole, it would have travelled due south from the North Pole to the equator, then west for a quarter of the way around Earth, and then due north back to the North Pole. In spherical geometry, the sum of angles inside a triangle is greater than 180° (in this example 270°, having arrived back at the north pole a 90° angle to the departure path) unlike on a flat surface, where it is always exactly 180°. == Weather


id: empirical-evidence-for-the-spherical-shape-of-earth-20260505-6
file: data/en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth-6.md
category: reference
title: Empirical evidence for the spherical shape of Earth
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Empirical_evidence_for_the_spherical_shape_of_Earth
date_saved: 2026-05-05T09:55:59.111630+00:00
instance: kb-cron
parent: empirical-evidence-for-the-spherical-shape-of-earth-20260505-0
summary:
=== Aircraft and spacecraft === People in high-flying aircraft or skydiving from high-altitude balloons can plainly see the curvature of Earth. Low-flying planes and commercial airliners do not necessarily fly high enough to make this obvious, especially when passenger windows narrow the field of view or clouds or terrain reduce the effective height from the visible surface. Trying to measure the curvature of the horizon by taking a picture is complicated by the fact that both windows and camera lenses can produce distorted images depending on the angle used. An extreme version of this effect can be seen in the fisheye lens. Scientific measurements would require a carefully calibrated lens. Photos of the ground taken from airplanes over a large enough area also do not fit seamlessly together on a flat surface, but do fit on a roughly spherical surface. Aerial photographs of large areas must be corrected to account for curvature. Many pictures have been taken of the entirety of Earth by satellites launched by a variety of governments and private organizations. From high orbits, where half the planet can be seen at once, it is plainly spherical. The only way to piece together all the pictures taken of


id: psychoanalytic-infant-observation-20260505-0
file: data/en.wikipedia.org/wiki/Psychoanalytic_infant_observation-0.md
category: reference
title: Psychoanalytic infant observation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Psychoanalytic_infant_observation
date_saved: 2026-05-05T09:56:00.301945+00:00
instance: kb-cron
parent: psychoanalytic-infant-observation-20260505-0
summary:
Psychoanalytic infant observation is a distinct empirical case study method in psychoanalytic and psychotherapy training which was developed at the Tavistock Clinic in London by child psychoanalyst Esther Bick. In 1948 she collaborated with John Bowlby to develop the approach as part of psychotherapy training. It has since become an essential feature of pre-clinical training in child and adult psychotherapy, psychoanalysis and related fields throughout the Western world. Psychoanalytic infant observation usually involves observing an infant and mother weekly over a two-year period beginning soon after birth until the child's second birthday. This naturalistic form of experiential enquiry provides a unique opportunity to sharpen and extend the observational skills of future therapists. Trainees learn first-hand how a relationship develops between babies and their family members and enables them to think about how babies grow physically, mentally and emotionally. The experience of observing family life is invaluable for professionals who later work with complex and disturbing presentations. == Rationale for the method == Infant Observation was the inspired initiative of Esther Bick. As a Child Psychoanalyst she pioneered this particular approach to studying babies in the midst of their family environment. In 1948, she began teaching at the Tavistock Clinic and


